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Stratospheric aerosols play an important role in the earth system and can affect the climate on timescales of months to years. However, estimating the characteristics of partially observed aerosol injections, such as those from volcanic eruptions, is fraught with uncertainties. This article presents a framework for stratospheric aerosol source inversion which accounts for background aerosol noise and earth system internal variability via a Bayesian approximation error approach. We leverage specially designed earth system model simulations using the Energy Exascale Earth System Model (E3SM). A comprehensive framework for data generation, data processing, dimension reduction, operator learning, and Bayesian inversion is presented where each component of the framework is designed to address particular challenges in stratospheric modeling on the global scale. We present numerical results using synthesized observational data to rigorously assess the ability of our approach to estimate aerosol sources and associate uncertainty with those estimates.

The application of a gate voltage to control the superconducting current flowing through a nanoscale superconducting constriction, named as gate-controlled supercurrent (GCS), has raised great interest for fundamental and technological reasons. To gain a deeper understanding of this effect and develop superconducting technologies based on it, the material and physical parameters crucial for GCS must be identified. Top-down fabrication protocols should be also optimized to increase device scalability, although studies suggest that top-down fabricated devices are more resilient to show GCS. Here, we investigate gated superconducting nanobridges made with a top-down fabrication process from thin films of the non-centrosymmetric superconductor NbRe. Unlike other devices previously reported, our NbRe devices systematically exhibit GCS, when made in specific conditions, which paves the way for higher device scalability. Our results also suggest that surface properties of NbRe nanobridges and their modification during fabrication are key for GCS., Comment: 14 pages, 4 figures

Here we demonstrate superconducting Dayem-bridge weak-links made of different stoichiometric compositions of NbRe. Our devices possess a relatively high critical temperature, normal-state resistance, and kinetic inductance. In particular, the high kinetic inductance makes this material a good alternative to more conventional niobium-based superconductors (e.g., NbN or NbTiN) for the realization of superinductors and high-quality factor resonators, whereas the high normal-state resistance yields a large output voltage in superconducting switches and logic elements realized upon this compound. Moreover, out-of-plane critical magnetic fields exceeding 2 T ensure that possible applications requiring high magnetic fields can also be envisaged. Altogether, these features make this material appealing for a number of applications in the framework of quantum technologies., Comment: 10 pages, 4 figures

We report on a novel material obtained by modifying pristine YBCO superconductor in solid phase synthesis via simultaneous partial substitution of Ba by Sr and O by Se. Simultaneous application of EDX and EBSD confirmed that Se atoms indeed enter the crystalline lattice cell. The detailed XRD analysis further confirmed this conclusion and revealed that the obtained polycrystalline material contains 5 phases, with the major phase ($>$30\%) being a cuprate YBa$_{1.4}$Sr$_{0.6}$Cu$_{3}$O$_{6}$Se$% _{0.51}$. The obtained superconductor demonstrates unique properties, including i) two superconducting transitions with $T_{c1}\approx$ 35 K (granular surface phase) and $T_{c2}\approx$ 13 K (bulk granular phase) - this granular phase arrangement naturally yields the Wohlleben effect; ii) reentrant diamagnetism and resistive state; iii) strong paramagnetism with Curie-Weiss behavior (% $\theta_{CW} \approx$ 4 K) and the ferromagnetic phase overruled by superconductivity; iv) Schottky anomaly visible in the heat capacity data and most likely delivered by small clusters of magnetic moments. Thorough analysis of the heat capacity data reveals a strong-coupling $d-$wave pairing in its bulk phase (with $2\Delta /T_{c}\approx 5$), and, most importantly, a very unusual anomaly in this cuprate. There are reasons to associate this anomaly with the quantum criticality observed in traditional cuprate superconductors at much higher fields (achievable only in certain laboratories). In our case, the fields leading to quantum criticality are much weaker ($\sim $7-9 T) thus opening avenues for exploration of the interplay between superconductivity and pair density waves by the wider research community.

A material called LK-99, a modified-lead apatite crystal structure with the composition Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O ($0.9

We report on magnetron deposition of thin superconducting rhenium films on sapphire substrates. During the deposition, substrates were held at ambient temperature. Critical temperature of the films is Tc~3.6 K. Films have polycrystalline structure, and grazing incidence X-ray diffractometry indicates that crystalline lattice parameters are somewhat larger compared to the bulk ones. Magnetoresistive and AC/DC susceptibilities allowed us to determine $H_{c1}$ and $H_{c2}$ of these films, as well as estimate coherence length $\xi$(0) and magnetic penetration depth $\lambda_L$(0). We also provide information on surface morphology of these films., Comment: arXiv admin note: text overlap with arXiv:2307.16313

We report on magnetron deposition and superconducting properties of a novel superconducting material: rhenium-lutetium films on sapphire substrates. Different compositions of Re$_{x}$Lu binary are explored from $x\approx 3.8$ to close to pure Re stoichiometry. The highest critical temperature, up to $T_{c}\approx $ 6.95 K, is obtained for $x\approx 10.5$. Depending on the deposition conditions, polycrystalline or amorphous films are obtainable, both of which are interesting for practice. Crystalline structure of polycrystalline phase is identified using grazing incidence X-ray diffractometry as a non-centrosymmetric superconductor. Superconducting properties were characterized both resistively and magnetically. Demonstration of superconductivity in this material justifies the point of view that Lu plays a role of group 3 transition metal in period 6 of the Periodic table of elements. In analogy with Re$_{0.82}$Nb$_{0.18}$, Re$_{6}$Ti, Re$_{6}$Hf and Re$_{6}$Zr, one can expect that crystalline Re--Lu is also breaking the time-reversal symmetry (this still waits confirmation). Magnetoresistivity and AC/DC susceptibility measurements allowed us to determine H$_{c1}$ and H$_{c2}$ of these films, as well as estimate coherence length $\xi (0)$ and magnetic penetration depth $\lambda _{L}(0)$. We also provide information on surface morphology of these films.

In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable. The identification of sources from observations of system state is vital for attribution and prediction, which inform critical policy decisions. The difficulty of these types of inverse problems lies in the inability to isolate sources and the cost of simulating computational models. Surrogate models may enable the many-query algorithms required for source identification, but data challenges arise from high dimensionality of the state and source, limited ensembles of costly model simulations to train a surrogate model, and few and potentially noisy state observations for inversion due to measurement limitations. The influence of auxiliary processes adds an additional layer of uncertainty that further confounds source identification. We introduce a framework based on (1) calibrating deep neural network surrogates to the flow maps provided by an ensemble of simulations obtained by varying sources, and (2) using these surrogates in a Bayesian framework to identify sources from observations via optimization. Focusing on an atmospheric dispersion exemplar, we find that the expressive and computationally efficient nature of the deep neural network operator surrogates in appropriately reduced dimension allows for source identification with uncertainty quantification using limited data. Introducing a variable wind field as an auxiliary process, we find that a Bayesian approximation error approach is essential for reliable source inversion when uncertainty due to wind stresses the algorithm., Comment: 29 pages, 10 figures

We designed and experimentally demonstrated a four-terminal superconducting device which can function as a non-latching (reversible) superconducting switch from the diode regime to the resistive state by applying a control current much smaller than the main transport current. The device utilizes a vortex-based superconducting diode mechanism which is switched back and forth via the injection of flux quanta through auxiliary current leads. Various applications in superconducting electronics can be foreseen.

The superconducting diode effect has been recently reported in a variety of systems and different symmetry breaking mechanisms have been examined. However, the frequency range of these potentially important devices still remains obscure. We investigated superconducting micro-bridges of Nb$_{3}$Sn in out-of-plane magnetic fields; optimum magnetic fields of $\sim$10 mT generate $\sim $10% diode efficiency, while higher fields of $\sim$15-20 mT quench the effect. The diode changes its polarity with magnetic field reversal. We documented superconductive diode rectification at frequencies up to 100 kHz, the highest reported as of today. Interestingly, the bridge resistance during diode operation reaches a value that is a factor of two smaller than in its normal state, which is compatible with the vortex-caused mechanism of resistivity. This is confirmed by finite element modeling based on time-dependent Ginzburg-Landau equations. To explain experimental findings, no assumption of lattice thermal inequilibrium was required. Dissimilar edges of the superconductor strip can be responsible for the inversion symmetry breaking by vortex penetration barrier; visual evidence of this opportunity was revealed by scanning electron microscopy. Estimates are in favor of much higher (GHz) range of frequencies for this type of diode.

Based on a time-dependent Ginzburg-Landau system of equations and finite element modeling, we present novel results related with the physics of phase-slippage in superconducting wires surrounded by a non-superconductive environment. These results are obtained within our previously reported approach related to superconducting rings and superconductive gravitational wave detector transducers. It is shown that the phase-slip centers (PSCs) can be effective in originating not only positive but also negative thermal fluxes. With an appropriate design utilizing thermal diodes, PSCs can serve as cryocooling engines. Operating at $T\sim 1$ K cryostat cold-finger, they can achieve sub-Kelvin temperatures without using $^3$He.

Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state variables. While proposed methods have assumed noise only in the dependent variables, experimental and numerical data for operator learning typically exhibit noise in the independent variables as well, since both variables represent signals that are subject to measurement error. In regression on scalar data, failure to account for noisy independent variables can lead to biased parameter estimates. With noisy independent variables, linear models fitted via ordinary least squares (OLS) will show attenuation bias, wherein the slope will be underestimated. In this work, we derive an analogue of attenuation bias for linear operator regression with white noise in both the independent and dependent variables. In the nonlinear setting, we computationally demonstrate underprediction of the action of the Burgers operator in the presence of noise in the independent variable. We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy independent variables for a variety of operator learning problems. Considering the Burgers operator in 1D and 2D, we demonstrate that EiV operator learning robustly recovers operators in high-noise regimes that defeat OLS operator learning. We also introduce an EiV model for time-evolving PDE discovery and show that OLS and EiV perform similarly in learning the Kuramoto-Sivashinsky evolution operator from corrupted data, suggesting that the effect of bias in OLS operator learning depends on the regularity of the target operator., Comment: 23 pages, 10 figures

Nonlocal vector calculus, which is based on the nonlocal forms of gradient, divergence, and Laplace operators in multiple dimensions, has shown promising applications in fields such as hydrology, mechanics, and image processing. In this work, we study the analytical underpinnings of these operators. We rigorously treat compositions of nonlocal operators, prove nonlocal vector calculus identities, and connect weighted and unweighted variational frameworks. We combine these results to obtain a weighted fractional Helmholtz decomposition which is valid for sufficiently smooth vector fields. Our approach identifies the function spaces in which the stated identities and decompositions hold, providing a rigorous foundation to the nonlocal vector calculus identities that can serve as tools for nonlocal modeling in higher dimensions., Comment: 31 pages

Modeling of phenomena such as anomalous transport via fractional-order differential equations has been established as an effective alternative to partial differential equations, due to the inherent ability to describe large-scale behavior with greater efficiency than fully-resolved classical models. In this review article, we first provide a broad overview of fractional-order derivatives with a clear emphasis on the stochastic processes that underlie their use. We then survey three exemplary application areas - subsurface transport, turbulence, and anomalous materials - in which fractional-order differential equations provide accurate and predictive models. For each area, we report on the evidence of anomalous behavior that justifies the use of fractional-order models, and survey both foundational models as well as more expressive state-of-the-art models. We also propose avenues for future research, including more advanced and physically sound models, as well as tools for calibration and discovery of fractional-order models., Comment: 75 pages, 16 figures

Partition of unity networks (POU-Nets) have been shown capable of realizing algebraic convergence rates for regression and solution of PDEs, but require empirical tuning of training parameters. We enrich POU-Nets with a Gaussian noise model to obtain a probabilistic generalization amenable to gradient-based minimization of a maximum likelihood loss. The resulting architecture provides spatial representations of both noiseless and noisy data as Gaussian mixtures with closed form expressions for variance which provides an estimator of local error. The training process yields remarkably sharp partitions of input space based upon correlation of function values. This classification of training points is amenable to a hierarchical refinement strategy that significantly improves the localization of the regression, allowing for higher-order polynomial approximation to be utilized. The framework scales more favorably to large data sets as compared to Gaussian process regression and allows for spatially varying uncertainty, leveraging the expressive power of deep neural networks while bypassing expensive training associated with other probabilistic deep learning methods. Compared to standard deep neural networks, the framework demonstrates hp-convergence without the use of regularizers to tune the localization of partitions. We provide benchmarks quantifying performance in high/low-dimensions, demonstrating that convergence rates depend only on the latent dimension of data within high-dimensional space. Finally, we introduce a new open-source data set of PDE-based simulations of a semiconductor device and perform unsupervised extraction of a physically interpretable reduced-order basis., Comment: 12 pages, 6 figures

Many active matter systems are known to perform L\'{e}vy walks during migration or foraging. Such superdiffusive transport indicates long-range correlated dynamics. These behavior patterns have been observed for microswimmers such as bacteria in microfluidic experiments, where Gaussian noise assumptions are insufficient to explain the data. We introduce \textit{active L\'evy swimmers} to model such behavior. The focus is on ideal swimmers that only interact with the walls but not with each other, which reduces to the classical L\'evy walk model but now under confinement. We study the density distribution in the channel and force exerted on the walls by the L\'evy swimmers, where the boundaries require proper explicit treatment. We analyze stronger confinement via a set of coupled kinetics equations and the swimmers' stochastic trajectories. Previous literature demonstrated that power-law scaling in a multiscale analysis in free space results in a fractional diffusion equation. We show that in a channel, in the weak confinement limit active L\'evy swimmers are governed by a modified Riesz fractional derivative. Leveraging recent results on fractional fluxes, we derive steady state solutions for the bulk density distribution of active L\'evy swimmers in a channel, and demonstrate that these solutions agree well with particle simulations. The profiles are non-uniform over the entire domain, in contrast to constant-in-the-bulk profiles of active Brownian and run-and-tumble particles. Our theory provides a mathematical framework for L\'evy walks under confinement with sliding no-flux boundary conditions and provides a foundation for studies of interacting active L\'evy swimmers., Comment: 25 pages, 4 figures

Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials. Motivated by this, we propose partition of unity networks (POUnets) which incorporate these elements directly into the architecture. Classification architectures of the type used to learn probability measures are used to build a meshfree partition of space, while polynomial spaces with learnable coefficients are associated to each partition. The resulting hp-element-like approximation allows use of a fast least-squares optimizer, and the resulting architecture size need not scale exponentially with spatial dimension, breaking the curse of dimensionality. An abstract approximation result establishes desirable properties to guide network design. Numerical results for two choices of architecture demonstrate that POUnets yield hp-convergence for smooth functions and consistently outperform MLPs for piecewise polynomial functions with large numbers of discontinuities., Comment: 8 pages, 5 figures

We analyze the well-posedness of an anisotropic, nonlocal diffusion equation. Establishing an equivalence between weighted and unweighted anisotropic nonlocal diffusion operators in the vein of unified nonlocal vector calculus, we apply our analysis to a class of fractional-order operators and present rigorous estimates for the solution of the corresponding anisotropic anomalous diffusion equation. Furthermore, we extend our analysis to the anisotropic diffusion-advection equation and prove well-posedness for fractional orders s in [0.5,1). We also present an application of the advection-diffusion equation to anomalous transport of solutes., Comment: 25 pages

We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and boundary conditions, but for which only scattered observations of the source term are available. Scattered observations of the solution may also be used in the regression. The framework combines co-kriging with the linear transformation of a Gaussian process together with the use of kernels given by spectral expansions in eigenfunctions of the boundary value problem. Thus, it benefits from a reduced-rank property of covariance matrices. We demonstrate that the resulting framework yields more accurate and stable solution inference as compared to physics-informed Gaussian process regression without boundary condition constraints., Comment: 23 pages, 9 figures

The results on the behavior of phase-slip centers in thin superconducting wires based on finite-element modeling and time-dependent Ginzburg-Landau (TDGL) equations are discussed. For closer relationship with experiments, we used finite-gap formulation of the TDGL system. Both the dynamic equation for the Cooper-pair condensate wave-function and the expression for the electric current are more complex than in the gapless case. On this basis, the influence of nonequilibrium phonons is explored. These phonons can essentially change the location of geometrical points in which the phase slippage takes place. They also affect the frequency of phase-slip oscillations. The reported effects are experimentally detectable and can be used in practical devices.

Strontium ruthenates have many similarities with copper oxide superconductors and are of particular interest for the investigation of the mechanisms and conditions which lead to high-temperature superconductivity. We report here on multiple experimental indications of superconductivity with onset at 40 K in strontium ruthenate doped by rhenium and selenium with chlorine used as the flux. The main experimental evidence arises from terahertz spectroscopy of this material followed by AC and DC magnetization, as well as measurements of its heat capacity and magnetoresistance. Structural and morphological studies revealed the heterophase nature of this polycrystalline material as well as the changes of lattice parameters relative to the original phases. Experimental data show a higher critical temperature on the surface compared to that of the bulk of the sample.

Second-order optimizers hold intriguing potential for deep learning, but suffer from increased cost and sensitivity to the non-convexity of the loss surface as compared to gradient-based approaches. We introduce a coordinate descent method to train deep neural networks for classification tasks that exploits global convexity of the cross-entropy loss in the weights of the linear layer. Our hybrid Newton/Gradient Descent (NGD) method is consistent with the interpretation of hidden layers as providing an adaptive basis and the linear layer as providing an optimal fit of the basis to data. By alternating between a second-order method to find globally optimal parameters for the linear layer and gradient descent to train the hidden layers, we ensure an optimal fit of the adaptive basis to data throughout training. The size of the Hessian in the second-order step scales only with the number weights in the linear layer and not the depth and width of the hidden layers; furthermore, the approach is applicable to arbitrary hidden layer architecture. Previous work applying this adaptive basis perspective to regression problems demonstrated significant improvements in accuracy at reduced training cost, and this work can be viewed as an extension of this approach to classification problems. We first prove that the resulting Hessian matrix is symmetric semi-definite, and that the Newton step realizes a global minimizer. By studying classification of manufactured two-dimensional point cloud data, we demonstrate both an improvement in validation error and a striking qualitative difference in the basis functions encoded in the hidden layer when trained using NGD. Application to image classification benchmarks for both dense and convolutional architectures reveals improved training accuracy, suggesting possible gains of second-order methods over gradient descent., Comment: 10 pages, 4 figures

Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources. As part of a broader effort in scientific machine learning, many recent works have incorporated physical constraints or other a priori information within Gaussian process regression to supplement limited data and regularize the behavior of the model. We provide an overview and survey of several classes of Gaussian process constraints, including positivity or bound constraints, monotonicity and convexity constraints, differential equation constraints provided by linear PDEs, and boundary condition constraints. We compare the strategies behind each approach as well as the differences in implementation, concluding with a discussion of the computational challenges introduced by constraints., Comment: 42 pages, 3 figures. Version 3: DOI & Reference added; appeared in Journal of Machine Learning for Modeling and Computing. Version 2 includes minor additions, clarifications and improvements to notation

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. In this work we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting provably robust data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity. Our scheme allows extraction of provably invertible nonlocal models whose kernels may be partially negative. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints and ensure that models are robust, even in small-data regimes. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels., Comment: 32 pages, 10 figures, 3 tables

Nonlocal and fractional-order models capture effects that classical partial differential equations cannot describe; for this reason, they are suitable for a broad class of engineering and scientific applications that feature multiscale or anomalous behavior. This has driven a desire for a vector calculus that includes nonlocal and fractional gradient, divergence and Laplacian type operators, as well as tools such as Green's identities, to model subsurface transport, turbulence, and conservation laws. In the literature, several independent definitions and theories of nonlocal and fractional vector calculus have been put forward. Some have been studied rigorously and in depth, while others have been introduced ad-hoc for specific applications. The goal of this work is to provide foundations for a unified vector calculus by (1) consolidating fractional vector calculus as a special case of nonlocal vector calculus, (2) relating unweighted and weighted Laplacian operators by introducing an equivalence kernel, and (3) proving a form of Green's identity to unify the corresponding variational frameworks for the resulting nonlocal volume-constrained problems. The proposed framework goes beyond the analysis of nonlocal equations by supporting new model discovery, establishing theory and interpretation for a broad class of operators, and providing useful analogues of standard tools from the classical vector calculus., Comment: 55 pages, 1 Figure. Improved version

Motivated by the gap between theoretical optimal approximation rates of deep neural networks (DNNs) and the accuracy realized in practice, we seek to improve the training of DNNs. The adoption of an adaptive basis viewpoint of DNNs leads to novel initializations and a hybrid least squares/gradient descent optimizer. We provide analysis of these techniques and illustrate via numerical examples dramatic increases in accuracy and convergence rate for benchmarks characterizing scientific applications where DNNs are currently used, including regression problems and physics-informed neural networks for the solution of partial differential equations., Comment: 26 pages

We prove and implement stochastic solution (or Feynman-Kac) formulas for boundary value problems involving the spectral fractional Laplacian with nonzero Dirichlet boundary condition. The main tools used in the proofs are the abstract Cauchy problem for Feller semigroups together with Balakrishnan's theory of fractional powers. We show the the spectral fractional Laplacian with nonzero Dirichlet boundary conditions is the generator of an appropriate Feller semigroup for subordinate stopped Brownian motion, and obtain a stochastic solution formula for the fractional heat equation in a bounded domain. We then obtain precise regularity and steady-state convergence properties of the parabolic problem using the eigenfunction expansion of the classical solution, which leads to estimates for the survival probability of subordinate stopped Brownian motion. These results allow us to take the long-time limit in the parabolic formula to establish a stochastic solution formula for the Dirichlet boundary value problem. These stochastic solution formulas for the spectral fractional Laplacian with nonzero boundary conditions are novel, and allow for efficient, embarrassingly parallel local solution of the above boundary value problems. We discuss the discretization of these formulas, and verify them in dimensions two and three with benchmark examples. We study the effect of the number of path samples and the time step size for path discretization on the accuracy of the solution., Comment: 45 pages

Study of the Abrikosov vortex motion in superconductors based on time-dependent Ginzburg-Landau equations reveals an opportunity to locally detect the values of the Aharonov-Bohm type curl-less vector potentials., Comment: The content of this article is presented at the APS March Meeting (New Orleans, 2017)

Data-driven discovery of "hidden physics" -- i.e., machine learning of differential equation models underlying observed data -- has recently been approached by embedding the discovery problem into a Gaussian Process regression of spatial data, treating and discovering unknown equation parameters as hyperparameters of a modified "physics informed" Gaussian Process kernel. This kernel includes the parametrized differential operators applied to a prior covariance kernel. We extend this framework to linear space-fractional differential equations. The methodology is compatible with a wide variety of fractional operators in $\mathbb{R}^d$ and stationary covariance kernels, including the Matern class, and can optimize the Matern parameter during training. We provide a user-friendly and feasible way to perform fractional derivatives of kernels, via a unified set of d-dimensional Fourier integral formulas amenable to generalized Gauss-Laguerre quadrature. The implementation of fractional derivatives has several benefits. First, it allows for discovering fractional-order PDEs for systems characterized by heavy tails or anomalous diffusion, bypassing the analytical difficulty of fractional calculus. Data sets exhibiting such features are of increasing prevalence in physical and financial domains. Second, a single fractional-order archetype allows for a derivative of arbitrary order to be learned, with the order itself being a parameter in the regression. This is advantageous even when used for discovering integer-order equations; the user is not required to assume a "dictionary" of derivatives of various orders, and directly controls the parsimony of the models being discovered. We illustrate on several examples, including fractional-order interpolation of advection-diffusion and modeling relative stock performance in the S&P 500 with alpha-stable motion via a fractional diffusion equation., Comment: 26 pages, 10 figures. In v2, a minor change to the formatting of a handful of references was made in the bibliography; the main text was unchanged. In v3, minor improvements were made to the exposition; more details about motivation, examples, optimization, and relation to previous works were given

The fractional Laplacian in R^d has multiple equivalent characterizations. Moreover, in bounded domains, boundary conditions must be incorporated in these characterizations in mathematically distinct ways, and there is currently no consensus in the literature as to which definition of the fractional Laplacian in bounded domains is most appropriate for a given application. The Riesz (or integral) definition, for example, admits a nonlocal boundary condition, where the value of a function u(x) must be prescribed on the entire exterior of the domain in order to compute its fractional Laplacian. In contrast, the spectral definition requires only the standard local boundary condition. These differences, among others, lead us to ask the question: "What is the fractional Laplacian?" We compare several commonly used definitions of the fractional Laplacian (the Riesz, spectral, directional, and horizon-based nonlocal definitions), and we use a joint theoretical and computational approach to examining their different characteristics by studying solutions of related fractional Poisson equations formulated on bounded domains. In this work, we provide new numerical methods as well as a self-contained discussion of state-of-the-art methods for discretizing the fractional Laplacian, and we present new results on the differences in features, regularity, and boundary behaviors of solutions to equations posed with these different definitions. We present stochastic interpretations and demonstrate the equivalence between some recent formulations. Through our efforts, we aim to further engage the research community in open problems and assist practitioners in identifying the most appropriate definition and computational approach to use for their mathematical models in addressing anomalous transport in diverse applications., Comment: 87 pages, 37 figures. Version 3: Minor corrections and improvements made

We present a blind multiframe image-deconvolution method based on robust statistics. The usual shortcomings of iterative optimization of the likelihood function are alleviated by minimizing the M-scale of the residuals, which achieves more uniform convergence across the image. We focus on the deconvolution of astronomical images, which are among the most challenging due to their huge dynamic ranges and the frequent presence of large noise-dominated regions in the images. We show that high-quality image reconstruction is possible even in super-resolution and without the use of traditional regularization terms. Using a robust \r{ho}-function is straightforward to implement in a streaming setting and, hence our method is applicable to the large volumes of astronomy images. The power of our method is demonstrated on observations from the Sloan Digital Sky Survey (Stripe 82) and we briefly discuss the feasibility of a pipeline based on Graphical Processing Units for the next generation of telescope surveys.

Fractional derivatives are nonlocal differential operators of real order that often appear in models of anomalous diffusion and a variety of nonlocal phenomena. Recently, a version of the Schr\"odinger Equation containing a fractional Laplacian has been proposed. In this work, we develop a Fractional Path Integral Monte Carlo algorithm that can be used to study the finite temperature behavior of the time-independent Fractional Schr\"odinger Equation for a variety of potentials. In so doing, we derive an analytic form for the finite temperature fractional free particle density matrix and demonstrate how it can be sampled to acquire new sets of particle positions. We employ this algorithm to simulate both the free particle and $^{4}$He (Aziz) Hamiltonians. We find that the fractional Laplacian strongly encourages particle delocalization, even in the presence of interactions, suggesting that fractional Hamiltonians may manifest atypical forms of condensation. Our work opens the door to studying fractional Hamiltonians with arbitrarily complex potentials that escape analytical solutions., Comment: 18 pages, 14 figures

We suggest here a method to detect gravitational waves (GW) different from the interferometric approach. It is based on two critical steps: conversion of the GW action into rotational motion and subsequent conversion into electric current. The ability to detect extremely tiny currents empowers this suggested approach in which the gravitational wave moves charges around closed loops. This new method may allow detection of gravitational waves with strain sensitivities beyond the reach of the interferometric approaches., Comment: 10 pages, 4 figures