Fast and accurate evaluation of Biot-Savart integrals over spatial curves in periodic domainsThe Biot-Savart law is relevant in physical contexts including electromagnetism and fluid dynamics. In the latter case, when the rotation of a fluid is confined to a set of very thin vortex…Juan Ignacio Polanco·Jun 11, 2024SaveLearn
A Multi-Scale Boltzmann Equation for Complex Systems of Neutral Gases across All Flow RegimesA Multi-scale Boltzmann Equation (MBE) is found from the gas-kinetic theory and the direct modeling philosophy as a master equation for complex physical systems of neutral gases across all flow…Sha Liu, Junzhe Cao, Sirui Yang et al.·Jun 11, 2024SaveLearn
Propagation in rough waveguides: Forward and inverse problemsWe discuss here the direct and inverse problems for wave propagation in a waveguide with rough internal surface and arbitrary mean shape. The high degree of multiple scattering inside the waveguide…Mark Spivack, Orsola Rath Spivack·Jun 10, 2024SaveLearn
Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum ComputersQuantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper…Zhao-Yun Chen, Teng-Yang Ma, Chuang-Chao Ye et al.·Jun 10, 2024SaveLearn
A high-performance reconstruction method for partially coherent ptychographyPtychography is now integrated as a tool in mainstream microscopy allowing quantitative and high-resolution imaging capabilities over a wide field of view. However, its ultimate performance is…Wenhui Xu, Shoucong Ning, Pengju Sheng et al.·Jun 9, 2024SaveLearn
A note on accurate pressure calculations of Coulomb systems with periodic boundary conditionsIn this note, we address some issues concerning the accurate pressure calculation of Coulomb systems with periodic boundary conditions. First, we prove that the formulas for the excess part of the…Lei Li, Jiuyang Liang, Zhenli Xu·Jun 9, 2024SaveLearn
HTESP (High-throughput electronic structure package): a Package for the high-throughput ab initio calculationsHigh-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online…Niraj K. Nepal, Paul C. Canfield, Lin-Lin Wang·Jun 6, 2024SaveLearn
The Associated Volume sampling algorithm as an alternative method for the calculation of ionisation cluster size distributions in computational nanodosimetryIn computational nanodosimetry, Monte Carlo Track Structure (MCTS) simulations are employed to calculate ionisation cluster size distributions (ICSDs), which are crucial for characterising mixed…João F. Canhoto, Yann Perrot, Reinhard Schulte et al.·Jun 6, 2024SaveLearn
Large-scale semi-discrete optimal transport with distributed Voronoi diagramsIn this article, we propose a numerical method to solve semi-discrete optimal transport problems for gigantic pointsets (108 points and more). By pushing the limits by several orders of magnitude, it…Bruno Lévy, Nicolas Ray, Quentin Mérigot et al.·Jun 6, 2024SaveLearn
Physics-Informed Neural Networks for the Numerical Modeling of Steady-State and Transient Electromagnetic Problems with Discontinuous MediaPhysics-informed neural networks (PINNs) have emerged as a promising numerical method based on deep learning for modeling boundary value problems, showcasing promising results in various fields. In…Michel Nohra, Steven Dufour·Jun 6, 2024SaveLearn
Equivariant Graph Neural Networks for Prediction of Tensor Material Properties of CrystalsModern E(3)-Equivariant networks may be used to predict rotationally equivariant properties, including tensorial quantities. Three such quantities: the dielectric, piezoelectric, and elasticity…Alex Heilman, Claire Schlesinger, Qimin Yan·Jun 5, 2024SaveLearn
Neural network sampling of Bethe-Heitler process in particle-in-cell codesThis study uses neural networks to improve Monte Carlo (MC) implementations of the Bethe-Heitler process in Particle-In-Cell (PIC) codes. We provide a neural network that is as accurate as…Óscar Amaro, Chiara Badiali, Bertrand Martinez·Jun 4, 2024SaveLearn
Astral: training physics-informed neural networks with error majorantsThe primal approach to physics-informed learning is a residual minimization. We argue that residual is, at best, an indirect measure of the error of approximate solution and propose to train with…Vladimir Fanaskov, Tianchi Yu, Alexander Rudikov et al.·Jun 4, 2024SaveLearn
Porting the grid-based 3D+3V hybrid-Vlasov kinetic plasma simulation Vlasiator to heterogeneous GPU architecturesVlasiator is a space plasma simulation code which models near-Earth ion-kinetic dynamics in three spatial and three velocity dimensions. It is highly parallelized, modeling the Vlasov equation…Markus Battarbee, Konstantinos Papadakis, Urs Ganse et al.·Jun 4, 2024SaveLearn
Quantized tensor networks for solving the Vlasov-Maxwell equationsThe Vlasov-Maxwell equations provide an ab-initio description of collisionless plasmas, but solving them is often impractical because of the wide range of spatial and temporal scales that…Erika Ye, Nuno Loureiro·Jun 4, 2024SaveLearn
Validating Automated Resonance Evaluation with Synthetic DataThe integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated…Oleksii Zivenko, Noah A. W. Walton, William Fritsch et al.·Jun 3, 2024SaveLearn
Efficient Computation Using Spatial-Photonic Ising Machines: Utilizing Low-Rank and Circulant Matrix ConstraintsWe explore the potential of spatial-photonic Ising machines (SPIMs) to address computationally intensive Ising problems that employ low-rank and circulant coupling matrices. Our results indicate that…Richard Zhipeng Wang, James S. Cummins, Marvin Syed et al.·Jun 3, 2024SaveLearn
Spectral scheme for atomic structure calculations in density functional theoryWe present a spectral scheme for atomic structure calculations in pseudopotential Kohn-Sham density functional theory. In particular, after applying an exponential transformation of the radial…Sayan Bhowmik, John E. Pask, Andrew J. Medford et al.·Jun 1, 2024SaveLearn
Machine Learning Conservation Laws of Dynamical systemsConservation laws are of great theoretical and practical interest. We describe a novel approach to machine learning conservation laws of finite-dimensional dynamical systems using trajectory data. It…Meskerem Abebaw Mebratie, Rüdiger Nather, Guido Falk von Rudorff et al.·May 31, 2024SaveLearn
A Recipe for Charge Density PredictionIn density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly…Xiang Fu, Andrew Rosen, Kyle Bystrom et al.·May 29, 2024SaveLearn
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse GrainingWe present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temperature response to…Blake R. Duschatko, Xiang Fu, Cameron Owen et al.·May 29, 2024SaveLearn
2D hydrodynamic simulation of TeraFETs beyond the gradual-channel approximation for transient, large-signal or ultrahigh-frequency simulationsIn the past decade, detection of THz radiation by plasma-wave-assisted frequency mixing in antenna-coupled field-effect transistors (TeraFETs) -- implemented in various semiconductor material systems…Florian Ludwig, Hartmut G. Roskos, Raul Borsche·May 29, 2024SaveLearn
Learning phase transitions by siamese neural networkThe wide application of machine learning (ML) techniques in statistics physics has presented new avenues for research in this field. In this paper, we introduce a semi-supervised learning method…Jianmin Shen, Shiyang Chen, Feiyi Liu et al.·May 27, 2024SaveLearn
Exploring the Nexus between Thermodynamic Phase Transitions and Geometric Fractals through Systematic Lattice Point ClassificationFractals are ubiquitous in the natural world, and their connection with phase transitions has been widely observed. This study investigates mechanisms of fractal formation from the perspective of…Yonglong Ding·May 24, 2024SaveLearn
Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural NetworksWe present a new class of equivariant neural networks, hereby dubbed Lattice-Equivariant Neural Networks (LENNs), designed to satisfy local symmetries of a lattice structure. Our approach develops…Giulio Ortali, Alessandro Gabbana, Imre Atmodimedjo et al.·May 22, 2024SaveLearn