Search

Recent advances in computing hardware and modeling software have given rise to new applications for numerical optimization. These new applications occasionally uncover bottlenecks in existing optimization algorithms and necessitate further specialization of the algorithms. However, such specialization requires expert knowledge of the underlying mathematical theory and the software implementation of existing algorithms. To address this challenge, we present modOpt, an open-source software framework that facilitates the construction of optimization algorithms from modules. The modular environment provided by modOpt enables developers to tailor an existing algorithm for a new application by only altering the relevant modules. modOpt is designed as a platform to support students and beginner developers in quickly learning and developing their own algorithms. With that aim, the entirety of the framework is written in Python, and it is well-documented, well-tested, and hosted open-source on GitHub. Several additional features are embedded into the framework to assist both beginner and advanced developers. In addition to providing stock modules, the framework also includes fully transparent implementations of pedagogical optimization algorithms in Python. To facilitate testing and benchmarking of new algorithms, the framework features built-in visualization and recording capabilities, interfaces to modeling frameworks such as OpenMDAO and CSDL, interfaces to general-purpose optimization algorithms such as SNOPT and SLSQP, an interface to the CUTEst test problem set, etc. In this paper, we present the underlying software architecture of modOpt, review its various features, discuss several educational and performance-oriented algorithms within modOpt, and present numerical studies illustrating its unique benefits., Comment: 37 pages with 13 figures. For associated code, see https://github.com/LSDOlab/modopt

We present an open-source Python framework for the shape optimization of complex shell structures using isogeometric analysis (IGA). IGA seamlessly integrates computer-aided design (CAD) and analysis models by employing non-uniform rational B-splines (NURBS) as basis functions, enabling the natural implementation of the Kirchhoff--Love shell model due to their higher order of continuity. We leverage the recently developed FEniCS-based analysis framework, PENGoLINS, for the direct structural analysis of shell structures consisting of a collection of NURBS patches through a penalty-based formulation. This contribution introduces the open-source implementation of gradient-based shape optimization for isogeometric Kirchhoff--Love shells with a modular architecture. Complex shell structures with non-matching intersections are handled using a free-form deformation (FFD) approach and a moving intersections formulation. The symbolic differentiation and code generation capabilities in FEniCS are utilized to compute the analytical derivatives. By integrating FEniCS with OpenMDAO, we build modular components that facilitate gradient-based shape optimization of shell structures. The modular architecture in this work supports future extensions and integration with other disciplines and solvers, making it highly customizable and suitable for a wide range of applications. We validate the design-analysis-optimization workflow through several benchmark problems and demonstrate its application to aircraft wing design optimization. The framework is implemented in a Python library named GOLDFISH (Gradient-based Optimization and Large-scale Design Framework for Isogeometric SHells) and the source code will be maintained at https://github.com/hanzhao2020/GOLDFISH., Comment: 39 pages, 14 figures

We developed Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Using the Doppler shift velocity, the continuum intensity, and the magnetic field observations from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI), we have created time-series datasets of acoustic power and magnetic flux, which are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease. Testing of the models' performance was done on data for 5 ARs, unseen from the models during training. Model 8, the best performing model trained, was able to make a successful prediction of emergence for all testing active regions in an experimental setting and three of them in an operational. The model predicted the emergence of AR11726, AR13165, and AR13179 respectively 10, 29, and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet areas on the solar disc. This work sets the foundations for ML-aided prediction of solar ARs., Comment: 20 pages, 8 figures, 5 tables, under review at the AAS Astrophysical Journal

The recent evidence for a stochastic gravitational wave background (GWB) in the nanohertz band, announced by pulsar timing array (PTA) collaborations around the world, has been posited to be sourced by either a population of supermassive black holes binaries or perturbations of spacetime near the inflationary era, generated by a zoo of various new physical phenomena. Gravitational waves (GWs) from these latter models would be explained by extensions to the standard model of cosmology and possibly to the standard model of particle physics. While PTA datasets can be used to characterize the parameter spaces of these models, energy conservation and limits from the cosmic microwave background (CMB) can be used $\textit{a priori}$ to bound those parameter spaces. Here we demonstrate that taking a simple rule for energy conservation and using CMB bounds on the radiation energy density can set stringent limits on the parameters for these models., Comment: 6 pages, 4 figures

Mixed-effects regression models represent a useful subclass of regression models for grouped data; the introduction of random effects allows for the correlation between observations within each group to be conveniently captured when inferring the fixed effects. At a time where such regression models are being fit to increasingly large datasets with many groups, it is ideal if (a) the time it takes to make the inferences scales linearly with the number of groups and (b) the inference workload can be distributed across multiple computational nodes in a numerically stable way, if the dataset cannot be stored in one location. Current Bayesian inference approaches for mixed-effects regression models do not seem to account for both challenges simultaneously. To address this, we develop an expectation propagation (EP) framework in this setting that is both scalable and numerically stable when distributed for the case where there is only one grouping factor. The main technical innovations lie in the sparse reparameterisation of the EP algorithm, and a moment propagation (MP) based refinement for multivariate random effect factor approximations. Experiments are conducted to show that this EP framework achieves linear scaling, while having comparable accuracy to other scalable approximate Bayesian inference (ABI) approaches., Comment: Changed author name from John Ormerod to John T. Ormerod to help with citations

We describe the new design and current performance of the Mid-InfraRed Spectrometer and Imager (MIRSI) on the NASA Infrared Telescope Facility (IRTF). The system has been converted from a liquid nitrogen/liquid helium cryogen system to one that uses a closed-cycle cooler, which allows it to be kept on the telescope at operating temperature and available for observing on short notice, requiring less effort by the telescope operators and day crew to maintain operating temperature. Several other enhancements have been completed, including new detector readout electronics, an IRTF-style standard instrument user interface, new stepper motor driver electronics, and an optical camera that views the same field as the mid-IR instrument using a cold dichroic mirror, allowing for guiding and/or simultaneous optical imaging. The instrument performance is presented, both with an engineering-grade array used from 2021-2023, and a science-grade array installed in the fall of 2023. Some sample astronomical results are also shown. The upgraded MIRSI is a facility instrument at the IRTF available to all users., Comment: 15 pages, 17 figures, accepted to PASP

The purpose of this paper is to characterize aiming errors in controlled weapon systems given target location as input. To achieve this objective, we analyze the accuracy of a controlled weapon system model for stationary and moving targets under different error sources and firing times. First, we develop a mathematical model of a gun turret and use it to design two controllers, a Proportional-Integral-Derivative controller and a Model Predictive controller, which accept the target location input and move the turret to the centroid of the target in simulations. For stationary targets, we analyze the impact of errors in estimating the system's parameters and uncertainty in the aim point measurement. Our results indicate that turret movement is more sensitive to errors in the moment of inertia than the damping coefficient, which could lead to incorrect simulations of controlled turret system accuracy. The results also support the hypothesis that turret movement errors are larger over longer distances of gun turret movement and, assuming no time constraints, accuracy improves the longer one waits to fire; though this may not always be practical in a combat scenario. Additionally, we demonstrate that the integral control component is needed for high accuracy in moving target scenarios., Comment: 29 pages, 15 figures

PySLSQP is a seamless interface for using the SLSQP algorithm from Python. It wraps the original SLSQP Fortran code sourced from the SciPy repository and provides a host of new features to improve the research utility of the original algorithm. Some of the additional features offered by PySLSQP include auto-generation of unavailable derivatives using finite differences, independent scaling of the problem variables and functions, access to internal optimization data, live-visualization, saving optimization data from each iteration, warm/hot restarting of optimization, and various other utilities for post-processing., Comment: 9 pages with 2 figures. For associated code, see https://github.com/anugrahjo/PySLSQP

Wire Arc Additive Manufacturing (WAAM) is a metal 3D printing technology that deposits molten metal wire on a substrate to form desired geometries. Articulated robot arms are commonly used in WAAM to produce complex geometric shapes. However, they mostly rely on proprietary robot and weld control software that limits process tuning and customization, incorporation of third-party sensors, implementation on robots and weld controllers from multiple vendors, and customizable user programming. This paper presents a general open-source software architecture for WAAM that addresses these limitations. The foundation of this architecture is Robot Raconteur, an open-source control and communication framework that serves as the middleware for integrating robots and sensors from different vendors. Based on this architecture, we developed an end-to-end robotic WAAM implementation that takes a CAD file to a printed WAAM part and evaluates the accuracy of the result. The major components in the architecture include part slicing, robot motion planning, part metrology, in-process sensing, and process tuning. The current implementation is based on Motoman robots and Fronius weld controller, but the approach is applicable to other industrial robots and weld controllers. The capability of the WAAM tested is demonstrated through the printing of parts of various geometries and acquisition of in-process sensor data for motion adjustment.

Many biological objects possess bilateral symmetry about a midline or midplane, up to a ``noise'' term. This paper uses landmark-based methods to measure departures from bilateral symmetry, especially for the two-group problem where one group is more asymmetric than the other. In this paper, we formulate our work in the framework of size-and-shape analysis including registration via rigid body motion. Our starting point is a vector of elementary asymmetry features defined at the individual landmark coordinates for each object. We introduce two approaches for testing. In the first, the elementary features are combined into a scalar composite asymmetry measure for each object. Then standard univariate tests can be used to compare the two groups. In the second approach, a univariate test statistic is constructed for each elementary feature. The maximum of these statistics lead to an overall test statistic to compare the two groups and we then provide a technique to extract the important features from the landmark data. Our methodology is illustrated on a pre-registered smile dataset collected to assess the success of cleft lip surgery on human subjects. The asymmetry in a group of cleft lip subjects is compared to a group of normal subjects, and statistically significant differences have been found by univariate tests in the first approach. Further, our feature extraction method leads to an anatomically plausible set of landmarks for medical applications., Comment: 20 pages

Wildfire smoke is a health hazard as it contains a mixture of carcinogenic volatile compounds and fine particulate matter. In particular, exposure to polycyclic aromatic hydrocarbons (PAHs) is a major concern, since these compounds have been recognized as important contributors to the overall carcinogenic risk of smoke exposure. In this work, gas and particle-phase PAH emissions from the combustion of Eastern White Pine (pinus strobus) were quantified using time-of-flight mass spectrometry over a range of burn conditions representative of wildfires and prescribed fires. These experiments allow for controlling conditions of fuel moisture, heat flux, and oxygen concentration to understand their impact on PAH emissions. We find that optimal conditions for fuel moisture content of 20 - 30%, heat load onto the sample of 60 - 70 kW/m$^2$, and oxygen concentrations of the burn environment of 5 - 15% can reduce the emissions of the heavy molar weight PAHs by up to 77%. Our analysis shows that the relative carcinogenic risk can be reduced by more than 50% under optimal conditions, offering a way for reducing emission exposure from forest treatment activities., Comment: Submitted to Atmospheric Pollution Research

While shape optimization using isogeometric shells exhibits appealing features by integrating design geometries and analysis models, challenges arise when addressing computer-aided design (CAD) geometries comprised of multiple non-uniform rational B-splines (NURBS) patches, which are common in practice. The intractability stems from surface intersections within these CAD models. In this paper, we develop an approach for shape optimization of non-matching isogeometric shells incorporating intersection movement. Separately parametrized NURBS surfaces are modeled using Kirchhoff--Love shell theory and coupled using a penalty-based formulation. The optimization scheme allows shell patches to move without preserving relative location with other members during the shape optimization. This flexibility is achieved through an implicit state function, and analytical sensitivities are derived for the relative movement of shell patches. The introduction of differentiable intersections expands the design space and overcomes challenges associated with large mesh distortion, particularly when optimal shapes involve significant movement of patch intersections in physical space. Throughout optimization iterations, all members within the shell structures maintain the NURBS geometry representation, enabling efficient integration of analysis and design models. The optimization approach leverages the multilevel design concept by selecting a refined model for accurate analysis from a coarse design model while maintaining the same geometry. We adopt several example problems to verify the effectiveness of the proposed scheme and demonstrate its applicability to the optimization of the internal stiffeners of an aircraft wing., Comment: 41 pages, 18 figures

High entropy oxides (HEO)s have garnered much interest due to their available high degree of tunability. Here, we study the local structure of (MgNiCuCoZn)0.167(MnCr)0.083O, a composition based on the parent HEO (MgNiCuCoZn)0.2O.We synthesized a series of thin films via pulsed laser deposition at incremental oxygen partial pressures. X-ray diffraction shows lattice parameter to decrease with increased pO2 pressures until the onset of phase separation. X-ray absorption fine structure shows that specific atomic species in the composition dictate the global structure of the material as Cr, Co, and Mn shift to energetically favorable coordination with increasing pressure. Transmission electron microscopy analysis on a lower-pressure sample exhibits a rock salt structure, but the higher-pressure sample reveals reflections reminiscent of the spinel structure. In all, these findings give a more complete picture on how (MgNiCuCoZn)0.167(MnCr)0.083O forms with varying initial conditions and advances fundamental knowledge of cation behavior in high entropy oxides.

Many record-breaking climate extremes arise from both greenhouse gas-induced warming and natural climate variability. Marine cloud brightening, a solar geoengineering strategy originally proposed to reduce long-term warming, could potentially mitigate extreme events by instead targeting seasonal phenomena, such as El Ni\~no-Southern Oscillation (ENSO). By exploiting the 2019-2020 Australian wildfire experiment-of-opportunity, we show that simulated marine cloud brightening in the southeast Pacific reproduces observed cloud changes and induces La Ni\~na-like responses. We then explore how cloud brightening timing and duration modifies the 1997-1998 and 2015-2016 El Ni\~no events. We find the earliest and longest interventions effectively restore neutral ENSO conditions and dampen El Ni\~no's impacts. Solar geoengineering that targets climate variability could complement tools such as ENSO forecasting and provide a pathway for climate risk mitigation.

Mamba state-space models (SSMs) were recently shown to outperform state-of-the-art (SOTA) Transformer large language models (LLMs) across various tasks. Despite subsequent widespread adaptation, little work has focused on Mamba LLMs' amenability for fine-tuning frameworks ubiquitously used for Transformer-based LLMs, e.g., mixed-precision fine-tuning (MPFT) and parameter-efficient fine-tuning (PEFT). For the former, it currently remains an open question whether Mamba's recurrent dynamics are robust to small input changes, such as those encountered during MPFT. Using dynamical systems theory (in particular, Lyapunov exponents), we answer this question in the affirmative. We empirically validate this result through several experiments, showing that Mamba SSMs are significantly more stable to changes introduced by mixed-precision than comparable Transformers, even when both MPFT and PEFT are combined. For PEFT, we show how targeting specific memory buffers in Mamba's customized CUDA kernels for low-rank adaptation regularizes SSM parameters, thus providing both parameter efficient learning and computational savings. Finally, with both MPFT and PEFT enabled, we explore the impact of instruction tuning Mamba SSMs for in-context learning (ICL) on natural language tasks. While pretrained Mamba and Mamba-2 models only achieve 38% and 82% (respectively) of the ICL improvements of comparable Transformer-based LLMs, we show that instruction tuning allows Mamba models to narrow this gap to 81% and Mamba-2 models to skyrocket over this gap to 132%., Comment: 18 pages, 4 figure panels, 5 tables

We unravel the distinct roles each cation plays in phase evolution, stability, and properties within Mg1/5Co1/5Ni1/5Cu1/5Zn1/5O high-entropy oxide (HEO) by integrating experimental findings, thermodynamic analyses, and first-principles predictions. Our approach is through sequentially removing one cation at a time from the five-component high-entropy oxide to create five four-component derivatives. Bulk synthesis experiments indicate that Mg, Ni, and Co act as rock salt phase stabilizers whereas only Mg and Ni enthalpically enhance single-phase rock salt stability in thin film growth; synthesis conditions dictate whether Co is a rock salt phase stabilizer or destabilizer. By examining the competing phases and oxidation state preferences using pseudo-binary phase diagrams and first-principles calculations, we resolve the stability differences between bulk and thin film for all compositions. We systematically explore HEO macroscopic property sensitivity to cation selection employing both predicted and measured optical spectra. This study establishes a framework for understanding high-entropy oxide synthesizability and properties on a per-cation basis that is broadly applicable to tailoring functional property design in other high-entropy materials., Comment: Saeed S. I. Almishal and Jacob T. Sivak contributed equally to this work

The recently introduced graph-accelerated non-intrusive polynomial chaos (NIPC) method has shown effectiveness in solving a broad range of uncertainty quantification (UQ) problems with multidisciplinary systems. It uses integration-based NIPC to solve the UQ problem and generates the quadrature rule in a desired tensor structure, so that the model evaluations can be efficiently accelerated through the computational graph transformation method, Accelerated Model evaluations on Tensor grids using Computational graph transformations (AMTC). This method is efficient when the model's computational graph possesses a certain type of sparsity which is commonly the case in multidisciplinary problems. However, it faces limitations in high-dimensional cases due to the curse of dimensionality. To broaden its applicability in high-dimensional UQ problems, we propose AS-AMTC, which integrates the AMTC approach with the active subspace (AS) method, a widely-used dimension reduction technique. In developing this new method, we have also developed AS-NIPC, linking integration-based NIPC with the AS method for solving high-dimensional UQ problems. AS-AMTC incorporates rigorous approaches to generate orthogonal polynomial basis functions for lower-dimensional active variables and efficient quadrature rules to estimate their coefficients. The AS-AMTC method extends AS-NIPC by generating a quadrature rule with a desired tensor structure. This allows the AMTC method to exploit the computational graph sparsity, leading to efficient model evaluations. In an 81-dimensional UQ problem derived from an air-taxi trajectory optimization scenario, AS-NIPC demonstrates a 30% decrease in relative error compared to the existing methods, while AS-AMTC achieves an 80% reduction.

This paper presents the development of a deep-atmosphere, nonhydrostatic dynamical core (DyCore) targeted towards ground-thermosphere atmospheric prediction. This DyCore is based on a novel formulation of the specific internal energy equation (SIEE), which, unlike standard potential temperature formulations, is valid for variable composition atmospheres. Two versions of a SIEE are derived from basic principles. The first version, which uses a product-rule (PR) continuity equation, contains an additional compressible term and does not conserve mass. The second version, which does not use the product-rule (No-PR) in the continuity equation, contains two compressible terms and conserves mass to machine precision regardless of time truncation error. The pressure gradient and gravitational forces in the momentum balance equation are formulated in a manner appropriate for HA applications and the spectral element method (SEM) is used with Implicit-Explicit (IMEX) and Horizontally Explicit Vertically Implicit (HEVI) time-integration. These new equation sets were implemented in two U. S. Navy atmospheric models: the Nonhydrostatic Unified Model of the Atmosphere (NUMA) and the Navy Environmental Prediction sysTem Using a Nonhydrostatic Core (NEPTUNE). Numerical results using a nonhydrostatic and hydrostatic baroclinic instability, a balanced zonal flow, and HA mountain wave experiments are shown. These results are compared to existing deep-atmosphere dynamical cores, indicating that the proposed discretized IEE equation sets are viable next-generation ground-to-thermosphere DyCores.

We distinguish the axiomatic study of proofs in geometry from study about geometry from general axioms for mathematics. We briefly report on an abuse of that distinction and its unfortunate effect on US high school education. We review a number of 20th century approaches to synthetic geometry. In doing so, we disambiguate (in the Wikipedia sense) the terms: metric, orthogonal, isotropic and hyperbolic. With some of these systems we are able to axiomatize `affine geometry' over the complex field (The argument is trivial from [Wu94] or [Szm78], but not remarked by either of them.). We examine the general question of the connections between axioms for Affine geometries and the stability classification of associated complete first order theories of fields. We conclude with reminiscences of a half-century friendship with Jan\'{o}s., Comment: preprint for collection

Many air-taxi concepts are capable of vertical takeoff and landing, enabling them to fly to and from urban locations. An important capability for these air taxis is the transition between hover and forward flight. We propose a robust methodology for computing optimal takeoff and transition trajectories using surrogate models trained on data from physics-based models. The use of surrogate models reduces the computational complexity and improves the robustness of the trajectory optimization algorithm. We demonstrate the versatility and robustness of the proposed methodology by applying it to 12 trajectory optimization problems that involve air-taxi takeoff and outbound transition. These trajectories are representative of real air-taxi operations, with a variety of constraints derived, in part, from proposed mission requirements.

Molecular-scale diodes made from self-assembled monolayers (SAMs) could complement silicon-based technologies with smaller, cheaper, and more versatile devices. However, advancement of this emerging technology is limited by insufficient electronic performance exhibited by the molecular current rectifiers. We overcome this barrier by exploiting the charge-transfer state that results from co-assembling SAMs of molecules with strong electron donor and acceptor termini. We obtain a substantial enhancement in current rectification, which correlates with the degree of charge transfer, as confirmed by several complementary techniques. These findings provide a previously unexplored method for manipulating the properties of molecular electronic devices by exploiting donor/acceptor interactions. They also serve as a model test platform for the study of doping mechanisms in organic systems. Our devices have the potential for fast widespread adoption due to their low-cost processing and self-assembly onto silicon substrates, which could allow seamless integration with current technologies.

Quantum state comparison, utilizing metrics like fidelity and trace distance, underpins the assessment of quantum networks within quantum information theory. While recent research has expanded theoretical understanding, incorporating error analysis and scalability considerations remains crucial for practical applications. The primary contribution of this letter is to address these gaps by deriving the novel operational trace distance for multi-node networks, establishing a trace distance vs. fidelity benchmark incorporating error bounds, and bridging quantum operations with tensor network analysis. We further explore the application of tensor network tools to quantum networks, offering new analytical avenues. This comprehensive approach provides a robust framework for evaluating quantum network performance under realistic error conditions, facilitating the development of reliable quantum technologies.

Industrial robotic applications such as spraying, welding, and additive manufacturing frequently require fast, accurate, and uniform motion along a 3D spatial curve. To increase process throughput, some manufacturers propose a dual-robot setup to overcome the speed limitation of a single robot. Industrial robot motion is programmed through waypoints connected by motion primitives (Cartesian linear and circular paths and linear joint paths at constant Cartesian speed). The actual robot motion is affected by the blending between these motion primitives and the pose of the robot (an outstretched/near-singularity pose tends to have larger path tracking errors). Choosing the waypoints and the speed along each motion segment to achieve the performance requirement is challenging. At present, there is no automated solution, and laborious manual tuning by robot experts is needed to approach the desired performance. In this paper, we present a systematic three-step approach to designing and programming a dual robot system to optimize system performance. The first step is to select the relative placement between the two robots based on the specified relative motion path. The second step is to select the relative waypoints and the motion primitives. The final step is to update the waypoints iteratively based on the actual measured relative motion. Waypoint iteration is first executed in simulation and then completed using the actual robots. For performance assessment, we use the mean path speed subject to the relative position and orientation constraints and the path speed uniformity constraint. We have demonstrated the effectiveness of this method on two systems, a physical testbed of two ABB robots and a simulation testbed of two FANUC robots, for two challenging test curves.

The univariate dimension reduction (UDR) method stands as a way to estimate the statistical moments of the output that is effective in a large class of uncertainty quantification (UQ) problems. UDR's fundamental strategy is to approximate the original function using univariate functions so that the UQ cost only scales linearly with the dimension of the problem. Nonetheless, UDR's effectiveness can diminish when uncertain inputs have high variance, particularly when assessing the output's second and higher-order statistical moments. This paper proposes a new method, gradient-enhanced univariate dimension reduction (GUDR), that enhances the accuracy of UDR by incorporating univariate gradient function terms into the UDR approximation function. Theoretical results indicate that the GUDR approximation is expected to be one order more accurate than UDR in approximating the original function, and it is expected to generate more accurate results in computing the output's second and higher-order statistical moments. Our proposed method uses a computational graph transformation strategy to efficiently evaluate the GUDR approximation function on tensor-grid quadrature inputs, and use the tensor-grid input-output data to compute the statistical moments of the output. With an efficient automatic differentiation method to compute the gradients, our method preserves UDR's linear scaling of computation time with problem dimension. Numerical results show that the GUDR is more accurate than UDR in estimating the standard deviation of the output and has a performance comparable to the method of moments using a third-order Taylor series expansion.

Recently, the graph-accelerated non-intrusive polynomial chaos (NIPC) method has been proposed for solving uncertainty quantification (UQ) problems. This method leverages the full-grid integration-based NIPC method to address UQ problems while employing the computational graph transformation approach, AMTC, to accelerate the tensor-grid evaluations. This method exhibits remarkable efficacy on a broad range of low-dimensional (three dimensions or less) UQ problems featuring multidisciplinary models. However, it often does not scale well with problem dimensions due to the exponential increase in the number of quadrature points when using the full-grid quadrature rule. To expand the applicability of this method to a broader range of UQ problems, this paper introduces a new framework for generating a tailored, partially tensor-structured quadrature rule to use with the graph-accelerated NIPC method. This quadrature rule, generated through the designed quadrature approach, possesses a tensor structure that is tailored for the computational model. The selection of the tensor structure is guided by an analysis of the computational graph, ensuring that the quadrature rule effectively capitalizes on the sparsity within the computational graph when paired with the AMTC method. This method has been tested on one 4D and one 6D UQ problem, both originating from aircraft design scenarios and featuring multidisciplinary models. Numerical results show that, when using with graph-accelerated NIPC method, our approach generates a partially tensor-structured quadrature rule that outperforms the full-grid Gauss quadrature and the designed quadrature methods (more than 40% reduction in computational costs) in both of the test problems.

Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be attributed to violation of one or more conversational principles. By drawing upon extensive research from both the social science and AI communities, we propose a set of maxims -- quantity, quality, relevance, manner, benevolence, and transparency -- for describing effective human-AI conversation. We first justify the applicability of the first four maxims (from Grice) in the context of human-AI interactions. We then argue that two new maxims, benevolence (concerning the generation of, and engagement with, harmful content) and transparency (concerning recognition of one's knowledge boundaries, operational constraints, and intents), are necessary for addressing behavior unique to modern human-AI interactions. We evaluate the degree to which various language models are able to understand these maxims and find that models possess an internal prioritization of principles that can significantly impact their ability to interpret the maxims accurately.

We study gravitational wave production during kinetic preheating after inflation with a focus on scenarios that arise in $\alpha$-attractor models where a scalar dilaton-like inflaton is kinetically coupled to a second scalar field. We present high-resolution lattice simulations of three $\alpha$-attractor models for a range of parameters to probe regions where preheating is efficient. We find that preheating in these models can be extremely violent, resulting in gravitational wave energy densities that can be constrained by cosmic microwave background measurements of the effective number of relativistic species, $N_{\rm eff}$, Comment: 13 pages, 10 figures

The multifaceted applications of polymers are often limited by their thermal conductivity. Therefore, understanding the mechanisms of thermal transport in polymers is of vital interest. Here, we leverage molecular layer deposition to grow three types of hybrid metalcone (i.e., alucone, zincone, and tincone) films and study their thermal and acoustic properties. The thermal conductivity of the hybrid polymer films ranges from 0.43 to 1.14 W/mK. Using kinetic theory, we trace the origin of thermal conductivity difference to sound speed change, which is dictated by the structural disorder within the films. Changing the disorder has negligible impacts on volumetric heat capacity and vibrational lifetimes. Our findings provide means to improve the thermal conductivity of organic, hybrid, and inorganic polymer films.

To create early warning capabilities for upcoming Space Weather disturbances, we have selected a dataset of 61 emerging active regions, which allows us to identify characteristic features in the evolution of acoustic power density to predict continuum intensity emergence. For our study, we have utilized Doppler shift and continuum intensity observations from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The local tracking of 30.66 x 30.66-degree patches in the vicinity of active regions allowed us to trace the evolution of active regions starting from the pre-emergence state. We have developed a machine learning model to capture the acoustic power flux density variations associated with upcoming magnetic flux emergence. The trained Long Short-Term Memory (LSTM) model is able to predict 5 hours ahead whether, in a given area of the solar surface, continuum intensity values will decrease. The performed study allows us to investigate the potential of the machine learning approach to predict the emergence of active regions using acoustic power maps as input., Comment: 9 pages, 4 figures, IAU Symposium 365 Proceedings

The class of Galileon scalar fields theories encapsulate the Vainshtein screening mechanism which is characteristic of a large range of infrared modified theories of gravity. Such theories can lead to testable departures from General Relativity through fifth forces and new scalar modes of gravitational radiation. However, the inherent non-linearity of the Vainshtein mechanism has limited analytic attempts to describe Galileon theories with both cubic and quartic interactions. To improve on this, we perform direct numerical simulations of the quartic Galileon model for a rotating binary source and infer the power spectrum of given multipoles. To tame numerical instabilities we utilize a low-pass filter, extending previous work on the cubic Galileon. Our findings show that the multipole expansion is well-defined and under control. Moreover, our results confirm that despite being a non-linear scalar, the dominant Galileon radiation is quadrupole, and we find a new scaling behaviour deep inside the Vainshtein region., Comment: 12 pages, 6 figures

The Galileon theory is a prototypical effective field theory that incorporates the Vainshtein screening mechanism--a feature that arises in some extensions of General Relativity, such as massive gravity. The Vainshtein effect requires that the theory contain higher order derivative interactions, which results in Galileons, and theories like them, failing to be technically well-posed. While this is not a fundamental issue when the theory is correctly treated as an effective field theory, it nevertheless poses significant practical problems when numerically simulating this model. These problems can be tamed using a number of different approaches: introducing an active low-pass filter and/or constructing a UV completion at the level of the equations of motion, which controls the high momentum modes. These methods have been tested on cubic Galileon interactions, and have been shown to reproduce the correct low-energy behavior. Here we show how the numerical UV-completion method can be applied to quartic Galileon interactions, and present the first simulations of the quartic Galileon model using this technique. We demonstrate that our approach can probe physics in the regime of the effective field theory in which the quartic term dominates, while successfully reproducing the known results for cubic interactions., Comment: 12 pages, 5 figures

YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO) has favorable macroscopic superconducting properties of $T_\mathrm{c}$ up to 93 K and $H_{c2}$ up to 150 T. However, its nanoscale electronic structure remains mysterious because bulk-like electronic properties are not preserved near the surface of cleaved samples for easy access by local or surface-sensitive probes. It has been hypothesized that Ca-doping at the Y site could induce an alternate cleavage plane that mitigates this issue. We use scanning tunneling microscopy (STM) to study both Ca-free and 10% Ca-doped YBCO, and find that the Ca-doped samples do indeed cleave on an alternate plane, yielding a spatially-disordered partial (Y,Ca) layer. Our density functional theory calculations support the increased likelihood of this new cleavage plane in Ca-doped YBCO. On this surface, we image a superconducting gap with average value 24 $\pm$ 3 meV and characteristic length scale 1-2 nm, similar to Bi-based high-$T_\mathrm{c}$ cuprates, but the first map of gap inhomogeneity in the YBCO family., Comment: corrected typo in metadata author name