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The ab initio path integral Monte Carlo (PIMC) method is one of the most successful methods in statistical physics, quantum chemistry and related fields, but its application to quantum degenerate Fermi systems is severely hampered by an exponential computational bottleneck: the notorious fermion sign problem. Very recently, Xiong and Xiong [J. Chem. Phys. 157, 094112 (2022)] have suggested to partially circumvent the sign problem by carrying out simulations of fictitious systems guided by an interpolating continuous variable ξ ∈ [−1, 1], with the physical Fermi- and Bose-statistics corresponding to ξ = −1 and ξ = 1. It has been proposed that information about the fermionic limit might be obtained by calculations within the bosonic sector ξ > 0 combined with an extrapolation throughout the fermionic sector ξ < 0, essentially bypassing the sign problem. Here, we show how the inclusion of the artificial parameter ξ can be interpreted as an effective penalty on the formation of permutation cycles in the PIMC simulation. We demonstrate that the proposed extrapolation method breaks down for moderate to high quantum degeneracy. Instead, the method constitutes a valuable tool for the description of large Fermi-systems of weak quantum degeneracy. This is demonstrated for electrons in a 2D harmonic trap and for the uniform electron gas (UEG), where we find excellent agreement (∼ 0.5 %) with exact configuration PIMC results in the high-density regime while attaining a speed-up exceeding 11 orders of magnitude. Finally, we extend the idea beyond the energy and analyze the radial density distribution (2D trap), as well as the static structure factor and imaginary-time density–density correlation function (UEG). [ABSTRACT FROM AUTHOR]

We consider the Boussinesq‐Peregrine (BP) system as described by Lannes [Lannes, D. (2013). The water waves problem: mathematical analysis and asymptotics (Vol. 188). American Mathematical Soc.], within the shallow water regime, and study the inverse problem of determining the time and space variations of the channel bottom profile, from measurements of the wave profile and its velocity on the free surface. A well‐posedness result within a Sobolev framework for (BP), considering a time dependent bottom, is presented. Then, the inverse problem is reformulated as a nonlinear PDE‐constrained optimization one. An existence result of the minimum, under constraints on the admissible set of bottoms, is presented. Moreover, an implementation of the gradient descent approach, via the adjoint method, is considered. For solving numerically both, the forward (BP) and its adjoint system, we derive a universal and low‐dissipation scheme, which contains non‐conservative products. The scheme is based on the FORCE‐ α$$ \alpha $$ method proposed in [Toro, E. F., Saggiorato, B., Tokareva, S., and Hidalgo, A. (2020). Low‐dissipation centred schemes for hyperbolic equations in conservative and non‐conservative form. Journal of Computational Physics, 416, 109545]. Finally, we implement this methodology to recover three different bottom profiles; a smooth bottom, a discontinuous one, and a continuous profile with a large gradient. We compare with two classical discretizations for (BP) and the adjoint system. These results corroborate the effectiveness of the proposed methodology to recover bottom profiles. [ABSTRACT FROM AUTHOR]

In this article, the potential Kadomtsev-Petviashvili (pKP) type coupled system with variable coefficients is studied, which have many applications in wave phenomena and soliton interactions in a two-dimensional space with time. In this framework, Hirota bilinear form is applied to acquire diverse types of interaction lump solutions from the foresaid equation. Abundant lump, lump-periodic, lump-soliton and multi soliton solutions to the pKP system are presented by the Hirota bilinear form and a mixture of exponentials and trigonometric functions. Lump, lump-periodic, lump-soliton and multi soliton solutions are studied with the usage of symbolic computation. In addition, the symbolic computation and the applied methods for governing model are investigated. The movement role of the waves is investigated, and the theoretical analysis of the acquired solutions is discussed using the bilinear technique of all produced solutions with 2D and 3D plots with respective parameters. The computational difficulties and outcomes highlight the clarity, effectiveness, and simplicity of the approaches, suggesting that these schemes can be applied to a variety of dynamic and static nonlinear equations governing evolutionary phenomena in computational physics as well as to other real-world situations and a wide range of academic fields. We used software Maple 2024 "(http://www.maplesoft.com/)". [ABSTRACT FROM AUTHOR]

Microwave ablation is becoming a standard procedure for treating tumors based on heat generation, causing an elevation in the tissue temperature level from 50 to 60 °C, causing tissue death. Microwave ablation is associated with uniform cell killing within ablation zones, multiple-antenna capability, low complication rates, and long-term survival. Several reports have demonstrated that multiple-antenna microwave ablation is a promising strategy for safely, rapidly, and effectively treating large tumors. The key advantage of multi-antenna tumor microwave ablation is the creation of a large, well-defined ablation zone without excessively long treatment times or high power that can damage healthy tissue. The strategic positioning of multiple probes provides a fully ablated volume, even in regions where individual probe damage is incomplete. Accurate modeling of the complex thermal and electromagnetic behaviors of tissue is critical for optimizing microwave ablation because material parameters and tissue responses can change significantly during the procedure. In the case of multi-antenna microwave ablation, the calculation complexity increases significantly, requiring significant computational resources and time. This study aimed to evaluate the efficacy and safety of liver percutaneous microwave ablation using the simultaneous activation of three antennas for the treatment of lesions larger than 3 cm. Based on the known results from a single-probe setup, researchers can estimate and evaluate various spatial configurations of the three-probe array to identify the optimal arrangement. Due to the synergistic effects of the combined radiation from the three antennas, the resulting ablation zone can be significantly larger, leading to better outcomes in terms of treatment time and effectiveness. The obtained results revealed that volumetric damage and the amount of damaged healthy tissue are smaller for a three-antenna configuration than for microwave ablation using a single-antenna and two-antenna configurations. [ABSTRACT FROM AUTHOR]

We present a problem to be assigned or done as an in-class activity in an upper-division undergraduate course on computational physics. The problem involves a home run hit by Mickey Mantle on May 22, 1963, which he famously called "the hardest ball I ever hit." Is this home run truly one for the record books, or has it been eclipsed by players in the modern era? Modeling the trajectory of a baseball involves consideration of both wind resistance and the Magnus effect and is an interesting application of numerical solutions to ordinary differential equations. Ultimately, the answer is that Mantle would compare favorably to the most powerful batters currently playing, but to arrive at that conclusion we must reflect on the plausibility of results and sources of uncertainty. Editor's Note: The physics of baseball is endlessly compelling. In this paper, the author analyzes a May 22, 1963, home-run hit by Mickey Mantle which struck a facade 118 feet above ground level at Yankee Stadium and nearly exited the park. Witnesses claimed that the ball was still rising when it struck the facade. This paper takes a numerical-analysis approach to attempting to determine the initial launch speed and angle of the ball, considering conditions of air density, temperature, relative humidity, wind, and spin. This is a fascinating case study of forensic physics subject to multiple uncertainties, and the reader's enjoyment is not diminished by the conclusion that witnesses were likely mistaken about the ball still rising. Appropriate for upper-level dynamics/computational physics students. [ABSTRACT FROM AUTHOR]

A univariate stochastic system driven by multiplicative Gaussian white noise is considered. The standard method for simulating its Langevin equation of motion involves incrementing the system's state variable by a biased Gaussian random number at each time step. It is shown that the efficiency of such simulations can be significantly enhanced by incorporating the skewness of the distribution of the updated state variable. A new algorithm based on this principle is introduced, and its superior performance is demonstrated using a model of free diffusion of a Brownian particle with a friction coefficient that decreases exponentially with the kinetic energy. The proposed simulation technique proves to be accurate over time steps that are an order of magnitude longer than those required by standard algorithms. The model used to test the new numerical technique is known to exhibit a transition from normal diffusion to superdiffusion as the environmental temperature rises above a certain critical value. A simple empirical formula for the time-dependent diffusion coefficient, which covers both diffusion regimes, is introduced, and its accuracy is confirmed through comparison with the simulation results. [ABSTRACT FROM AUTHOR]

We look at computational physics from an electrical engineering perspective and suggest that several concepts of mathematics, not so well-established in computational physics literature, present themselves as opportunities in the field. We discuss elliptic complexes and highlight the category theoretical background and its role as a unifying language between algebraic topology, differential geometry, and modelling software design. In particular, the ubiquitous concept of naturality is central. Natural differential operators have functorial analogues on the cochains of triangulated manifolds. In order to establish this correspondence, we derive formulas involving simplices and barycentric coordinates, defining discrete vector fields and a discrete Lie derivative as a result of a discrete analogue of Cartan’s magic formula. This theorem is the main mathematical result of the paper. [ABSTRACT FROM AUTHOR]

The physics and computational prediction of turbulent boundary layer flow over axisymmetric and three-dimensional bodies are examined. Three cases were considered for which extensive experimental results and companion Reynolds-averaged Navier Stokes (RANS) solutions were obtained and/or available in the open literature. These cases all have Reynolds numbers based upon the freestream velocity and body geometric scale on the order of $ 10^5 $ 10 5 to $ 10^6 $ 10 6 , which is large for laboratory scales but small compared to the maximum scales observed for full-scale vehicles. Despite significant differences in approach flow fields and geometries for these three cases, some common themes emerged in the findings. All cases involved complications due to pressure gradients combined with streamwise curvature, and all exhibited regions of turbulence reduction due to accelerated flow. These complications led to discrepancies in computed results even in attached flow regions where it is often assumed that RANS models provide reliable predictions. The authors recommend further work on modelling approaches that can capture rapid distortion effects on turbulence transport that can be incorporated into industry-useful frameworks. Two cases involving laterally symmetric, three-dimensional wall-mounted hills with aft-body separation revealed that asymmetric mean flow fields are likely to result. This finding has been observed in experiments conducted in multiple facilities and in computations using multiple solvers and turbulence models. It is concluded that non-unique and asymmetric global flow solutions are fundamental to flow cases with lateral geometric symmetry involving turbulent boundary layer separation. Further work is also needed to accurately predict low-frequency unsteadiness due to geometries that produce non-unique mean flow fields. For such flows, it remains to be definitively determined whether experimentally observed modes of the mean flow are equivalent, or nearly equivalent, to asymmetric mean flow solutions obtained using RANS approaches. [ABSTRACT FROM AUTHOR]

Numerical prediction of the interactions between wind and ocean waves is essential for climate modeling and a wide range of offshore operations. Large Eddy Simulation (LES) of the marine atmospheric boundary layer is a practical numerical predictive tool but requires parameterization of surface fluxes at the air–water interface. Current momentum flux parameterizations primarily use wave-phase adapting computational grids, incurring high computational costs, or use an equilibrium model based on Monin–Obukhov similarity theory for rough surfaces that cannot resolve wave phase information. To include wave phase-resolving physics at a cost similar to the equilibrium model, the MOving Surface Drag (MOSD) model is introduced. It assumes ideal airflow over locally piece-wise planar representations of moving water wave surfaces. Horizontally unresolved interactions are still modeled using the equilibrium model. Validation against experimental and numerical datasets with known monochromatic waves demonstrates the robustness and accuracy of the model in representing wave-induced impacts on mean velocity and Reynolds stress profiles. The model is formulated to be applicable to a broad range of wave fields and its ability to represent cross-swell and multiple wavelength cases is illustrated. Additionally, the model is applied to LES of a laboratory-scale fixed-bottom offshore wind turbine model, and the results are compared with wind tunnel experimental data. The LES with the MOSD model shows good agreement in wind–wave–wake interactions and phase-dependent physics at a low computational cost. The model's simplicity and minimal computational needs make it valuable for studying turbulent atmospheric-scale flows over the sea, particularly in offshore wind energy research. [ABSTRACT FROM AUTHOR]

We have used a combination of pico-to-nano temporal/spatial scale computational physics and chemistry modeling of plasma–material interfaces in the tokamak fusion plasma edges to unravel the evolving characteristics, not readily accessible by empirical means, of lithium-, oxygen-, and hydrogen-containing materials of plasma-facing components under irradiation by hydrogen and its isotopes. In the present calculation, amorphous lithium compound surfaces containing oxygen, Li2O, and LiOH were irradiated by 1–100 eV particles at incident angles on the surface ranging from perpendicular to almost grazing angles. Consequential surface processes, reflection, retention, and sputtering were studied at "the same footing" and compared to earlier results from amorphous Li and LiH surfaces. The critical role of charging dynamics of lithium, oxygen, and hydrogen atoms in the surface chemistry during hydrogen-fuel irradiation was found to drive the kinetics and dynamics of these surfaces in unexpected ways that ultimately could have profound effects on fusion plasma confinement behavior and surface erosion. [ABSTRACT FROM AUTHOR]

Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how intercellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue engineering. Here, by combining an agent-based model for contractile cells on elastic substrates with endothelial cell culture experiments, we show that substrate deformation-mediated mechanical interactions between cells can cluster and align them into branched networks. Motivated by the structure and function of vasculogenic networks, we predict how measures of network connectivity like percolation probability and fractal dimension as well as local morphological features including junctions, branches, and rings depend on cell contractility and density and on substrate elastic properties including stiffness and compressibility. We predict and confirm with experiments that cell network formation is substrate stiffness dependent, being optimal at intermediate stiffness. We also show the agreement between experimental data and predicted cell cluster types by mapping a combined phase diagram in cell density substrate stiffness. Overall, we show that long-range, mechanical interactions provide an optimal and general strategy for multicellular self-organization, leading to more robust and efficient realizations of space-spanning networks than through just local intercellular interactions.

The finite element discretization of computational physics problems frequently involves the manual generation of an initial mesh and the application of adaptive mesh refinement (AMR). This approach is employed to selectively enhance the accuracy of resolution in regions that encompass significant features throughout the simulation process. In this paper, we introduce Adaptnet, a Graph Neural Networks (GNNs) framework for learning mesh generation and adaptation. The model is composed of two GNNs: the first one, Meshnet, learns mesh parameters commonly used in open-source mesh generators, to generate an initial mesh from a Computer Aided Design (CAD) file; while the second one, Graphnet, learns mesh-based simulations to predict the components of an Hessian-based metric to perform anisotropic mesh adaptation. Our approach is tested on structural (Deforming plate–Linear elasticity) and fluid mechanics (Flow around cylinders–steady-state Stokes) problems. Our findings demonstrate the model's ability to precisely predict the dynamics of the system and adapt the mesh as needed. The adaptability of the model enables learning resolution-independent mesh-based simulations during training, allowing it to scale effectively to more intricate state spaces during inference. [ABSTRACT FROM AUTHOR]

The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and is the discretization engine powering many computational physics and engineering applications across a number of domains. This paper describes some of the recent research and development in MFEM, focusing on performance portability across leadership-class supercomputing facilities, including exascale supercomputers, as well as new capabilities and functionality, enabling a wider range of applications. Much of this work was undertaken as part of the Department of Energy's Exascale Computing Project (ECP) in collaboration with the Center for Efficient Exascale Discretizations (CEED). [ABSTRACT FROM AUTHOR]

In the literature, there are many algorithms for the computation of Feynman diagrams (Hahn, Nucl. Phys. B Proc. Suppl. 89, 231–236 2000; Smirnov and Zeng, Comput. Phys. Commun. 302, 109261 2024; Patel, Comput. Phys. Commun. 197, 276–290 2015). QCD Sum Rules, however, differ from standard loop computations in several key aspects. One of the fundamental distinctions of the QCD Sum Rules technique is the inclusion of the Borel transformation (Shifman et al. Nucl. Phys. B 147, 448–518 1979). To address this critical component, the borelT package is developed in Mathematica (Wolfram Research, Inc., 2023) , ensuring the integration of the Borel transformation into the algorithms. [ABSTRACT FROM AUTHOR]

This research explores some modernistic soliton solutions to the (3+1)-dimensional periodic potential the Gross–Pitaevskii equation with a truncated M-fractional derivative plays a significant role in Bose–Einstein condensation, which describes the dynamics of the condensate wave function. The obtained results include trigonometric, hyperbolic trigonometric and exponential function solutions. Three techniques named: the expafunction method, the Sardar sub-equation method, and the extended (G'/G)-expansion approach are employed to achieve a variety of new solutions for the governing model. More comprehensive information about the dynamical representation of some of the solutions is being presented by visualizing the 2D, 3D and contour plots. This work reveals a number of new types of traveling-wave solutions, such as the double periodic singular, the periodic singular, the dark singular, the dark kink singular, the periodic solitary singular, and the singular soliton solutions. These novel solutions are not the same as those that were previously studied for this governing equation. The presented techniques demonstrate clarity, efficacy, and simplicity, revealing their relevance to diverse sets of dynamic and static nonlinear equations pertaining to evolutionary events in computational physics, in addition to other real-world applications and a wide range of study fields for addressing a variety of other nonlinear fractional models that hold significance in the fields of applied science and engineering. [ABSTRACT FROM AUTHOR]

This article analyzes the dynamics of waves to a new higher dimensional Kadomtsev−Petviashvili equation. The higher dimensional Kadomtsev−Petviashvili equation and its expansions have attracted a great deal of scientific interest during the past few decades. Several nonlinear phenomena in a range of domains, like the dynamics of long waves with modest amplitudes in oceans and plasma physics, are studied using this family. In this study, we successfully apply the Hirota bilinear method with the adoption of several test strategies. A set of results like breather, two‐wave, and lump periodic solutions are secured. To visually depict the output, a variety of graphs featuring distinct shapes are produced in response to appropriate parameter values. The computational complexities and results emphasize the transparency, effectiveness, and ease of the technique, indicating the method's applicability to many kinds of both static and dynamic nonlinear equations regarding evolutionary phenomena in computational physics, as well as other practical domains and research fields. [ABSTRACT FROM AUTHOR]

The nonlinear Schrödinger equation (NLSE) is the most significant physical model to explain the fluctuations of optical soliton proliferation in optical fiber theory. Optical soliton propagation in nonlinear fibers is currently a subject of great interest due to the multiple prospects for ultrafast signal routing systems and short light pulses in communications. In this article, the time-fractional perturbed NLSE that demonstrates the super fast wave propagation in optical fibers is investigated analytically. To better understand the underlying mechanisms for these kinds of nonlinear systems, the results are displayed using 3D, 2D, and contour graphics. Furthermore, it is confirmed that the established results are stable, and the modulation instability for the governing model is also studied. The computational intricacies and results highlight the clarity, efficacy, and simplicity of the approaches, pointing to the applicability of these methods to various sets of dynamic and static nonlinear equations governing evolutionary phenomena in computational physics, as well as to other practical domains and a variety of research fields. [ABSTRACT FROM AUTHOR]

Advancements in Artificial Intelligence (AI), particularly in Machine Learning (ML), are increasingly being utilized to address complex challenges in spacecraft technologies. One of the fundamental areas of focus is the thermal regulation system, which is crucial for the optimal functioning of the spacecraft. This paper introduces an innovative approach that employs Physics-Informed Neural Networks (PINNs) to model and predict temperature distributions within spacecraft. Unlike traditional neural networks, PINNs incorporate known physical laws, in this case, the heat equation, to ensure that the model's predictions are both accurate and physically consistent. We present a detailed methodology for developing a PINN that can accurately predict temperature variations over time, using sparse and noisy temperature data from a spacecraft. The proposed model is trained using a combination of data-driven and physics-informed loss functions, ensuring that the predictions adhere to the underlying physics of heat transfer. The results demonstrate the efficacy of PINNs in improving the accuracy of thermal predictions, which can significantly enhance the performance of the spacecraft's thermal regulation system. This work paves the way for further integration of physics-informed ML models in various aspects of spacecraft design and operation, offering a robust framework for tackling complex physical phenomena in space exploration. [ABSTRACT FROM AUTHOR]

Claudio Mendoza (1951-2024) was a renowned computational physicist known for his humor, irreverence, and intellectual activity. He was born in Venezuela and grew up in a small village where his family, along with other artists and intellectuals, created an environment of intense intellectual activity. Claudio pursued his education in London and later returned to Venezuela to work as a faculty member before completing his Ph.D. at University College London. He made significant contributions to projects on atomic data for astrophysical applications and X-ray spectroscopy. Claudio faced challenges in his career due to his public opinions, leading to his retirement and emigration to the United States. He passed away in 2024 after battling lung cancer. [Extracted from the article]

Teaching computation in science courses can enhance science education, but doing so requires that teachers expand the vision of their discipline beyond the traditional view of science presented in most curricula. This article describes a design-based research (DBR) program that included collaboration among high school teachers and professional development leaders in physics and computer science education. Through three years of professional development and teacher-led development, field testing, and refinement of integrated curricular resources, we have combined instructional modeling practices, physical lab materials, and computer programming activities. One of the outcomes is a co-created framework for the integration of computational modeling into physics that is sensitive to teachers' interests and expressed needs in addition to learning goals. This framework merges two evidence-based approaches to teaching: Bootstrap:Algebra, a web-based computing curriculum that emphasizes using multiple representations of functions and scaffolds that make the programming process explicit, and Modeling Instruction in physics, an approach that emphasizes the use of conceptual models, modeling practices and representational tools. In doing so, we uncover the need to balance teachers' visions for integration opportunities with practical instructional needs and emphasize that frameworks for integration need to reflect teachers' values and goals. [ABSTRACT FROM AUTHOR]

Recent advances in automated algebra for dilute Fermi gases in the virial expansion, where coarse temporal lattices were found advantageous, motivate the study of more general computational schemes that could be applied to arbitrary densities, beyond the dilute limit where the virial expansion is physically reasonable. We propose here such an approach by developing what we call the Quantum Thermodynamics Computational Engine (QTCE). In QTCE, the imaginary-time direction is discretized and the interaction is accounted for via a quantum cumulant expansion, where the coefficients are expressed in terms of non-interacting expectation values. The aim of QTCE is to enable the systematic resolution of interaction effects at fixed temporal discretization, as in lattice Monte Carlo calculations, but here in an algebraic rather than numerical fashion. Using this approach, in combination with numerical integration techniques (both known and alternative ones proposed here), we explore the thermodynamics of spin-1/2 fermions across spatial dimensions, focusing on the unitary limit. We find that, remarkably, extremely coarse temporal lattices, when suitably renormalized using known results from the virial expansion, yield stable partial sums for QTCE's cumulant expansion that are qualitatively and quantitatively correct in wide regions (when compared with known experimental results). This article is part of the theme issue 'The liminal position of Nuclear Physics: from hadrons to neutron stars'. [ABSTRACT FROM AUTHOR]

Nature finds ways to realize multi-functional surfaces by modulating nano-scale patterns on their surfaces, enjoying transparent, bactericidal, and/or anti-fogging features. Therein height distributions of nanopatterns play a key role. Recent advancements in nanotechnologies can reach that ability via chemical, mechanical, or optical fabrications. However, they require laborious complex procedures, prohibiting fast mass manufacturing. This paper presents a computational framework to help design multi-functional nano patterns by light. The framework behaves as a surrogate model for the inverse design of nano distributions. The framework's hybrid (i.e., human and artificial) intelligence-based approach helps learn plausible rules of multi-physics processes behind the UV-controlled nano patterning and enriches training data sets. Then the framework's inverse machine learning (ML) model can describe the required UV doses for the target heights of liquid in nano templates. Thereby, the framework can realize multiple functionalities including the desired nano-scale color, frictions, and bactericidal properties. Feasibility test results demonstrate the promising capability of the framework to realize the desired height distributions that can potentially enable multi-functional nano-scale surface properties. This computational framework will serve as a multi-physics surrogate model to help accelerate fast fabrications of nanopatterns with light and ML. Modulating the height distribution of nanostructures imparts multiple functions to the surface, but the recent methods are too complex for rapid mass production. This paper introduces a framework combining human and artificial intelligence to design multi-functional surfaces with tailored properties like color, friction, and bactericidal effects using capillary force lithography. [ABSTRACT FROM AUTHOR]

In order to offer guidelines for physics-informed neural network (PINN) implementation, this study presents a comprehensive review of PINN, an emerging field at the intersection of deep learning and computational physics. PINN offers a novel approach to solve physics problems by leveraging the flexibility and scalability of neural networks, even with small or no data. First, a general description of different physics problem types and target tasks addressable with PINN was provided. A generic PINN architecture was described in detail using a component-wise approach, with components ranging from collocation points to optimization methods. Then, we surveyed studies that sought to improve upon each of these components. To offer practical insights, we highlighted studies that focused on key issues of PINN implementation and showcased three practical applications. Lastly, a summary and potential research directions were provided to offer guidelines for reliable and customized PINN implementations. [ABSTRACT FROM AUTHOR]

The editorial discusses the challenge of defining interdisciplinary physics and its evolving nature. It explores the hierarchical approach of physics, emphasizing experiments and synthesis. The Research Topic includes papers on interdisciplinary research in sociophysics, econophysics, social inequality, and the Hirsch index. The articles highlight the importance of collaboration across disciplines and the need to remove artificial boundaries for scientific progress. [Extracted from the article]

This Perspective presents an overview of the development of the hierarchy of Davydov's Ansätze and a few of their applications in many-body problems in computational chemical physics. Davydov's solitons originated in the investigation of vibrational energy transport in proteins in the 1970s. Momentum-space projection of these solitary waves turned up to be accurate variational ground-state wave functions for the extended Holstein molecular crystal model, lending unambiguous evidence to the absence of formal quantum phase transitions in Holstein systems. The multiple Davydov Ansätze have been proposed, with increasing Ansatz multiplicity, as incremental improvements of their single-Ansatz parents. For a given Hamiltonian, the time-dependent variational formalism is utilized to extract accurate dynamic and spectroscopic properties using Davydov's Ansätze as its trial states. A quantity proven to disappear for large multiplicities, the Ansatz relative deviation is introduced to quantify how closely the Schrödinger equation is obeyed. Three finite-temperature extensions to the time-dependent variation scheme are elaborated, i.e., the Monte Carlo importance sampling, the method of thermofield dynamics, and the method of displaced number states. To demonstrate the versatility of the methodology, this Perspective provides applications of Davydov's Ansätze to the generalized Holstein Hamiltonian, variants of the spin-boson model, and systems of cavity-assisted singlet fission, where accurate dynamic and spectroscopic properties of the many-body systems are given by the Davydov trial states. [ABSTRACT FROM AUTHOR]

Density Functional Theory (DFT) sees prominent use in computational chemistry and physics; however, problems due to the self-interaction error (SIE) pose additional challenges to obtaining qualitatively correct results. As an unphysical energy an electron exerts on itself, the SIE impacts most practical DFT calculations. We conduct an in-depth analysis of the one-electron SIE in which we replicate delocalization effects for simple geometries. We present a simple visualization of such effects, which may help in future qualitative analysis of the one-electron SIE. By increasing the number of nuclei in a linear arrangement, the SIE increases dramatically. We also show how molecular shape impacts the SIE. Two- and three-dimensional shapes show an even greater SIE stemming mainly from the exchange functional with some error compensation from the one-electron error, which we previously defined [D. R. Lonsdale and L. Goerigk, Phys. Chem. Chem. Phys. 22, 15805 (2020)]. Most tested geometries are affected by the functional error, while some suffer from the density error. For the latter, we establish a potential connection with electrons being unequally delocalized by the DFT methods. We also show how the SIE increases if electrons occupy higher-lying atomic orbitals; seemingly one-electron SIE free methods in a ground are no longer SIE free in excited states, which is an important insight for some popular, non-empirical density functional approximations (DFAs). We conclude that the erratic behavior of the SIE in even the simplest geometries shows that robust DFAs are needed. Our test systems can be used as a future benchmark or contribute toward DFT development. [ABSTRACT FROM AUTHOR]

Droplet impact dynamics is an interfacial phenomenon that is shown everywhere in nature and is the underlying of numerous technological applications including bio-printing, tissue engineering, pharmaceuticals, fight against COVID-19 pandemic, smart biomaterials, and flexible electronics. Over the last decade, expeditious advancement of novel functional interfacial surfaces, high-speed visualization, nanoscience, nanotechnology, machine learning, and computational power, as well as the connection of flow physics with interfacial science, have contributed to enhancing the understanding of relevant complex physical phenomena. Droplet, upon impacting onto substrates, can deposit, spread, bounce, and splash. Features of droplet impact physics and surface wettability necessitate elaborate solid–liquid interactions. Given the significance of droplet impact physics for healthcare and electronics, it is recommended for the scientific community to direct research studies to profound the understanding of such complex physics. Therefore, this Review initially focuses on liquid–solid interfacial science. Second, droplet impact physics on numerous solid surfaces was discussed. Substrates with various wettability and physical features were considered: hydrophilic, hydrophobic, superhydrophobic, smooth, rough, and flexible elastic surfaces. Furthermore, numerous advancements of droplet impact on solid surfaces related to advanced technologies and challenges including printed electronics, smart biomaterials, tissue engineering, machine learning, and COVID-19 pandemic were reviewed. Finally, this Review outlines future perspectives and research directions in complex droplet impact physics. [ABSTRACT FROM AUTHOR]

A statistical emulator can be used as a surrogate of complex physics-based calculations to drastically reduce the computational cost. Its successful implementation hinges on an accurate representation of the nonlinear response surface with a high-dimensional input space. Conventional "space-filling" designs, including random sampling and Latin hypercube sampling, become inefficient as the dimensionality of the input variables increases, and the predictive accuracy of the emulator can degrade substantially for a test input distant from the training input set. To address this fundamental challenge, we develop a reliable emulator for predicting complex functionals by active learning with error control (ALEC). The algorithm is applicable to infinite-dimensional mapping with high-fidelity predictions and a controlled predictive error. The computational efficiency has been demonstrated by emulating the classical density functional theory (cDFT) calculations, a statistical-mechanical method widely used in modeling the equilibrium properties of complex molecular systems. We show that ALEC is much more accurate than conventional emulators based on the Gaussian processes with "space-filling" designs and alternative active learning methods. In addition, it is computationally more efficient than direct cDFT calculations. ALEC can be a reliable building block for emulating expensive functionals owing to its minimal computational cost, controllable predictive error, and fully automatic features. [ABSTRACT FROM AUTHOR]

The balance of processes affecting electron density drives the dynamics of upper‐atmospheric electrical events, such as sprites. We examine the detachment of electrons from negatively charged atomic oxygen (O−) via collisions with neutral molecular nitrogen (N2) leading to the formation of nitrous oxide (N2O). Past research posited that this process, even without significant vibrational excitation of N2, strongly impacts the dynamics of sprites. We introduce updated rate coefficients derived from recent experimental measurements which suggest a negligible influence of this reaction on sprite dynamics. Given that previous rates were incompatible with the observed decay of the light emissions from sprite glows, our findings support that glows actually result from electron depletion in sprite columns. Plain Language Summary: Sprites are transient, filamentary luminous structures appearing between approximately 50 and 85 km above Earth's surface. While the primary sprite activity is ephemeral, lasting mere thousandths of a second, certain luminous features persist up to a hundred times longer. The key to understanding these enduring structures lies in the evolution of free electron populations that facilitate electrical conductivity. Here we show that a process that influences this population is slower than previously thought. This may explain why luminous structures can persist for so long. Key Points: Associative detachment of electrons in sprites proceeds almost exclusively from vibrationally excited N2We provide updated rate coefficients for electron associative detachmentIn models with the updated rates, sprite glows persist tens of milliseconds, in agreement with observations [ABSTRACT FROM AUTHOR]

Much has been written about Adaptive Learning, but does its implementation alone guarantee success? We have found that integrating an Adaptive Learning Strategy with diverse didactic techniques gives better results. The objectives of this exploratory study were to know the impact of the Adaptive Learning Strategy on students' learning and achievement of disciplinary and transversal sub-competencies in courses supported by an Adaptive Platform in the School of Engineering and Sciences at Tecnologico de Monterrey. The assessment of the students' and professors' experience with an Adaptive Learning Strategy evaluated platform's usability, teaching, learning, and engagement. The study employed a mixed methodological approach, sequential Quant- > Qual, and was quasi-experimental, with control and experimental groups. The courses that participated in the intervention were Computational Thinking, Physics I, Physics II, and Fundamental Mathematical Modeling. The findings indicated that implementing an innovation like Adaptive Learning positively impacts students' learning and improvement when integrating elements of a flipped classroom, self-regulated learning, and micro-learning into an Adaptive Learning Strategy. The authors also propose an Implementation Model of the Adaptive Learning Strategy that has been designed by the university, implemented, and evaluated successfully. Highlights: The Adaptive Learning Strategy tends to impact students' learning levels and gain positively. A comprehensive Adaptive Learning Strategy should include elements of the flipped classroom, self-regulated learning, and micro-learning. Implementing an Adaptive Learning Strategy requires a carefully applied delivery model to ensure a successful learning experience for students. [ABSTRACT FROM AUTHOR]

The risk of well control is high when displacement gas kick occurs in the deep fractured reservoir, and improper handling can easily lead to blowout, which will seriously affect the deep well drilling. Using Fluent software, a simulation model was constructed to depict the interaction of a horizontal well with vertical fractures. The displacement gas kick process was simulated in horizontal well under formation temperature and pressure conditions. An analysis was conducted to assess the impact of fracture width, quantity, as well as the viscosity and density of drilling fluid on the process of gas-liquid displacement. It further compared the effectiveness of three measures in counteracting displacement gas kick: applying back pressure at the wellhead, altering the viscosity and density of the drilling fluid. New findings suggest that the gas-liquid interface within the fracture exhibits a funnel-like shape, where the gas and liquid phases are layered in the annulus and manifest in a streak-like pattern. Fracture width, quantity, and drilling fluid density promote displacement, while drilling fluid viscosity inhibits the displacement. Different from vertical wells, in horizontal wells, displacement gas kick mainly appears in the underbalanced interval. The fracture width primarily determines the size of the displacement window, while the density and viscosity of the drilling fluid exert lesser influence. Horizontal wells are highly sensitive to variations in external conditions when it comes to displacement gas kick. Therefore, enhancing the wellhead back pressure is advisable to address the displacement gas kick in horizontal wells. [ABSTRACT FROM AUTHOR]

Soft matter mechanics generally involve finite deformations and instabilities of structures in response to a wide range of mechanical and non-mechanical stimuli. Modeling plates and shells is generally a challenge due to their geometrically nonlinear response to loads; however, non-mechanical loads further complicate matters as it is often not clear how they modify the shell's energy functional. In this work, we demonstrate how to form a mechanical interpretation of these non-mechanical stimuli, in which the standard shell strain energy can be augmented with potentials corresponding to how a non-mechanical stimulus acts to change the shell's area and curvature via the natural stretch and curvature. As a result, the effect of non-mechanical stimuli to deform shells is transformed into effective external loadings, and this framework allows for the application of analytical and computational tools that are standard within the mechanics community. Furthermore, we generalize the effect of mass change during biological growth to account for its effect on the stress constitution. The theory is formulated based on a standard, stress-free reference configuration which is known a priori, meaning it can be physically observed, and only requires the solution of a single-field equation, the standard mechanical momentum or equilibrium equation, despite capturing the effects of non-mechanical stimuli. We validate the performance of this model by several benchmark problems, and finally, we apply it to complex examples, including the snapping of the Venus flytrap, leaf growth, and the buckling of electrically active polymer plates. Overall, we expect that mechanicians and non-mechanicians alike can use the approach presented here to quickly modify existing computational tools with effective external loadings calculated in this novel theory to study how various types of non-mechanical stimuli impact the mechanics and physics of thin shell structures. [ABSTRACT FROM AUTHOR]

In this paper, the Gilson-Pickering equation which enables a wave propagation in plasma physics and crystal lattice theory is studied. Based on the auxiliary transformation, the sets of the nonlinear form of ODE along with some analytic solutions are deriven. Multi wave solutions containing one-, two- and three-waves are obtained. The characterizations of new waves, crystal lattice theory and plasma physics using by modified extended tanh-function method are investigated. Through an approach, some solitary solutions are reached. Meanwhile, influence of the coefficients in that equation for the periodic and soliton is illustrated. The effect of the free variables on the behavior of reached plots to a few solutions including exact cases is also explored depending upon the nature of nonlinearities. The dynamic properties of the obtained results are shown and analyzed by some density, two and three-dimensional images. The computational difficulties and outcomes highlight the clarity, effectiveness, and simplicity of the approaches, suggesting that these schemes can be applied to a variety of dynamic and static nonlinear equations governing evolutionary phenomena in computational physics as well as to other real-world situations and a wide range of academic fields. We believe that our results would pave a way for future research generating optical memories based on the nonlinear solitons. [ABSTRACT FROM AUTHOR]

To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. Instead, we propose that EBM parameterizations, as a lossy data compression problem, can be better formulated using established physical rules widely used in computational physics and chemistry, and different biogeochemical processes can be more robustly integrated within a reactive‐transport framework. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process‐level hypotheses to test in empirical studies. Finally, while empirical models with more parameters are often less robust, physical rules‐based models can be more robust and show lower predictive equifinality, stemming from their enhanced consistency in representations of processes, interactions and spatial scaling. Plain Language Summary: Robust ecosystem biogeochemical models are needed to provide humanity with predictions to understand and manage interactions between terrestrial ecosystems and the climate. However, existing models do not fully achieve this target because of their wide use of statistical relationships derived from empirical observations. We argue that wider adoption of physical rules can help develop better ecosystem biogeochemical models to meet with society's needs. This can be achieved by deeper interdisciplinary collaboration between scientists from fields in soils, biology, chemistry, physics, and mathematics. Development of improved biogeochemical models will better position society to adapt to climate change. Key Points: The popular empirically based modeling approaches limit improvement of existing ecosystem biogeochemical model predictionsPhysical rules‐based approaches will help develop scaling consistent ecosystem biogeochemical modelsInter‐disciplinary collaboration can accelerate development and adoption of physical rules‐based ecosystem biogeochemical models [ABSTRACT FROM AUTHOR]

Coarse-grained models describe the macroscopic mean response of a process at large scales, which derives from stochastic processes at small scales. Common examples include accounting for velocity fluctuations in a turbulent fluid flow model and cloud evolution in climate models. Most existing techniques for constructing coarse-grained models feature ill-defined parameters whose values are arbitrarily chosen (e.g., a window size), are narrow in their applicability (e.g., only applicable to time series or spatial data), or cannot readily incorporate physics information. Here, we introduce the concept of physics-guided Gaussian process regression as a machine-learning-based coarse-graining technique that is broadly applicable and amenable to input from known physics-based relationships. Using a pair of case studies derived from molecular dynamics simulations, we demonstrate the attractive properties and superior performance of physics-guided Gaussian processes for coarse-graining relative to prevalent benchmarks. The key advantage of Gaussian-process-based coarse-graining is its ability to seamlessly integrate data-driven and physics-based information. [ABSTRACT FROM AUTHOR]

Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations. [ABSTRACT FROM AUTHOR]