Inverse Radon transforms: analytical and Tikhonov-like regularizations of inversionWe study the influence of analytical regularization used in the generalized function (distribution) space to the Tikhonov regularization procedure utilized in the different versions of…I. V. Anikin, Xurong Chen·May 22, 2024SaveLearn
Multi-Frequency Progressive Refinement for Learned Inverse ScatteringInterpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical…Owen Melia, Olivia Tsang, Vasileios Charisopoulos et al.·May 21, 2024SaveLearn
Glassy dynamics in deep neural networks: A structural comparisonDeep Neural Networks (DNNs) share important similarities with structural glasses. Both have many degrees of freedom, and their dynamics are governed by a high-dimensional, non-convex landscape…Max Kerr Winter, Liesbeth M. C. Janssen·May 21, 2024SaveLearn
Nonlocal free-energy density functional for warm dense matterFinite-temperature orbital-free density functional theory (FT-OFDFT) holds significant promise for simulating warm dense matter due to its favorable scaling with both system size and temperature.…Cheng Ma, Min Chen, Yu Xie et al.·May 21, 2024SaveLearn
Geant4: a Game Changer in High Energy Physics and Related Applicative FieldsGeant4 is an object-oriented toolkit for the simulation of the passage of particles through matter. Its development was initially motivated by the requirements of physics experiments at high energy…Tullio Basaglia, Zane W. Bell, Daniele D'Agostino et al.·May 20, 2024SaveLearn
Role of correlations in the maximum distribution of multiscale stationary Markovian processesWe are interested in investigating the statistical properties of extreme values for strongly correlated variables. The starting motivation is to understand how the strong-correlation properties of…Salvatore Miccichè·May 19, 2024SaveLearn
First principles simulations of dense hydrogenAccurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g. planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory…Michael Bonitz, Jan Vorberger, Mandy Bethkenhagen et al.·May 17, 2024SaveLearn
Symmetry adaptation for self-consistent many-body calculationsThe exploitation of space group symmetries in numerical calculations of periodic crystalline solids accelerates calculations and provides physical insight. We present results for a space-group…Xinyang Dong, Emanuel Gull·May 15, 2024SaveLearn
Performance of wave function and Green's functions based methods for non equilibrium many-body dynamicsTheoretical descriptions of non equilibrium dynamics of quantum many-body systems essentially employ either (i) explicit treatments, relying on truncation of the expansion of the many-body wave…Cian C. Reeves, Gaurav Harsha, Avijit Shee et al.·May 14, 2024SaveLearn
Adaptive Time Stepping for the Two-Time Integro-Differential Kadanoff-Baym EquationsThe non-equilibrium Green's function gives access to one-body observables for quantum systems. Of particular interest are quantities such as density, currents, and absorption spectra which are…Thomas Blommel, David J. Gardner, Carol S. Woodward et al.·May 14, 2024SaveLearn
The TDHF code Sky3D version 1.2The Sky3D code has been widely used to describe nuclear ground states, collective vibrational excitations, and heavy-ion collisions. The approach is based on Skyrme forces or related energy density…Abhishek, Paul Stevenson, Yue Shi et al.·May 14, 2024SaveLearn
Online Test-time Adaptation for Interatomic PotentialsMachine learning interatomic potentials (MLIPs) enable more efficient molecular dynamics (MD) simulations with ab initio accuracy, which have been used in various domains of physical science.…Taoyong Cui, Chenyu Tang, Dongzhan Zhou et al.·May 14, 2024SaveLearn
A multiscale hybrid Maxwellian-Monte-Carlo Coulomb collision algorithm for particle simulationsCoulomb collisions in particle simulations for weakly coupled plasmas are modeled by the Landau-Fokker-Planck equation, which is typically solved by Monte-Carlo (MC) methods. One of the main…G. Chen, A. J. Stanier, L. Chacón et al.·May 14, 2024SaveLearn
Undisturbed velocity recovery with transient and weak inertia effects in volume-filtered simulations of particle-laden flowsIn volume-filtered Euler-Lagrange simulations of particle-laden flows, the fluid forces acting on a particle are estimated using reduced models, which rely on the knowledge of the local undisturbed…Fabien Evrard, Akshay Chandran, Ricardo Cortez et al.·May 13, 2024SaveLearn
LATTE: an atomic environment descriptor based on Cartesian tensor contractionsWe propose a new descriptor for local atomic environments, to be used in combination with machine learning models for the construction of interatomic potentials. The Local Atomic Tensors Trainable…Franco Pellegrini, Stefano de Gironcoli, Emine Küçükbenli·May 13, 2024SaveLearn
Multiresolution of the one dimensional free-particle propagator. Part 1: ConstructionThe free-particle propagator, a key operator in various algorithms for simulating the time evolution of the Schr\"odinger equation, is studied. A multiscale approximation of this propagator is…Evgueni Dinvay, Yuliya Zabelina, Luca Frediani·May 13, 2024SaveLearn
Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic Shower SimulationsAmong the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via…Max Aehle, Mihály Novák, Vassil Vassilev et al.·May 13, 2024SaveLearn
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale SystemMolecular dynamics (MD) simulations have transformed our understanding of the nanoscale, driving breakthroughs in materials science, computational chemistry, and several other fields, including…Kylee Santos, Stan Moore, Tomas Oppelstrup et al.·May 13, 2024SaveLearn
Phase separation in a binary mixture of sticky spheresWe numerically investigate the dependence of range of attractive potential on the phase separation of 2-D binary systems. Through extensive simulations and analysis, we show that when the range of…D. C. Thakur, Jalim Singh, A. V. Anil Kumar·May 13, 2024SaveLearn
Transferable Neural Wavefunctions for SolidsDeep-Learning-based Variational Monte Carlo (DL-VMC) has recently emerged as a highly accurate approach for finding approximate solutions to the many-electron Schr\"odinger equation. Despite its…Leon Gerard, Michael Scherbela, Halvard Sutterud et al.·May 13, 2024SaveLearn
Discrete Lehmann representation of three-point functionsWe present a generalization of the discrete Lehmann representation (DLR) to three-point correlation and vertex functions in imaginary time and Matsubara frequency. The representation takes the form…Dominik Kiese, Hugo U. R. Strand, Kun Chen et al.·May 9, 2024SaveLearn
Benchmarking Geant4 photonuclear process model for the photo-induced reaction of deformed nuclei in the GDR regionThe Geant4 photonuclear process is benchmarked by comparing it with experimental data to verify the ability of the Geant4 toolkit to simulate the photo-induced reaction on deformed nuclei in the…P. D. Khue, P. V. Cuong, D. L. Balabanski et al.·May 9, 2024SaveLearn
Understanding solid nitrogen through machine learning simulationWe construct a fast, transferable, general purpose, machine-learning interatomic potential suitable for large-scale simulations of N2. The potential is trained only on high quality quantum…Marcin Kirsz, Ciprian G. Pruteanu, Peter I. C. Cooke et al.·May 8, 2024SaveLearn
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?This study investigates the potential accuracy boundaries of physics-informed neural networks, contrasting their approach with previous similar works and traditional numerical methods. We find that…Jorge F. Urbán, Petros Stefanou, José A. Pons·May 7, 2024SaveLearn
High Energy Density Radiative Transfer in the Diffusion Regime with Fourier Neural OperatorsRadiative heat transfer is a fundamental process in high energy density physics and inertial fusion. Accurately predicting the behavior of Marshak waves across a wide range of material properties and…Joseph Farmer, Ethan Smith, William Bennett et al.·May 7, 2024SaveLearn