Quantitative phase and absorption contrast imagingWe present an algorithm for coherent diffractive imaging with phaseless measurements. It treats the forward model as a combination of coherent and incoherent waves. The algorithm reconstructs…Miguel Moscoso, Alexei Novikov, George Papanicolaou et al.·Mar 23, 2022SaveLearn
Applications of physics informed neural operatorsWe present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural…Shawn G. Rosofsky, Hani Al Majed, E. A. Huerta·Mar 23, 2022SaveLearn
Rapidly Encoding Generalizable Dynamics in a Euclidean Symmetric Neural NetworkSlinky, a helical elastic rod, is a seemingly simple structure with unusual mechanical behavior; for example, it can walk down a flight of stairs under its own weight. Taking Slinky as a test-case,…Qiaofeng Li, Tianyi Wang, Vwani Roychowdhury et al.·Mar 22, 2022SaveLearn
An Efficient Data-Driven Multiscale Stochastic Reduced Order Modeling Framework for Complex SystemsSuitable reduced order models (ROMs) are computationally efficient tools in characterizing key dynamical and statistical features of nature. In this paper, a systematic multiscale stochastic ROM…Changhong Mou, Nan Chen, Traian Iliescu·Mar 22, 2022SaveLearn
Flow-matching -- efficient coarse-graining of molecular dynamics without forcesCoarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to…Jonas Köhler, Yaoyi Chen, Andreas Krämer et al.·Mar 21, 2022SaveLearn
Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonatorsResonances are omnipresent in physics and essential for the description of wave phenomena. We present an approach for computing eigenfrequency sensitivities of resonances. The theory is based on…Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt et al.·Mar 21, 2022SaveLearn
Orientation Adaptive Minimal Learning Machine: Application to Thiolate-Protected Gold Nanoclusters and Gold-Thiolate RingsMachine learning (ML) force fields are one of the most common applications of ML methods in the field of physical and chemical science. In the optimal case, they are able to reach accuracy close to…Antti Pihlajamäki, Sami Malola, Tommi Kärkkäinen et al.·Mar 18, 2022SaveLearn
radioactivedecay: A Python package for radioactive decay calculationsradioactivedecay is a Python package for radioactive decay modelling. It contains functions to fetch decay data, define inventories of nuclides and perform decay calculations. The default nuclear…Alex Malins, Thom Lemoine·Mar 18, 2022SaveLearn
Quantum perturbation theory using Tensor cores and a deep neural networkTime-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural…Joshua Finkelstein, Emanuel H. Rubensson, Susan M. Mniszewski et al.·Mar 17, 2022SaveLearn
Critical-point phenomena and finite-size scaling in mean-field equal-coupling photonic networksThe mean-field optical phase transition in multimode equal-coupling photonic networks is studied by temporal evolution of the nonlinear equations of motion of the coupled modes. Analogies to…Oliver Melchert·Mar 17, 2022SaveLearn
Improved analysis of converging shock radiographsWe previously reported an experimental platform to induce a spherically-convergent shock in a sample using laser-driven ablation, probed with time-resolved x-ray radiography, and an analysis method…Damian C. Swift, Andrea L. Kritcher, Amy Lazicki et al.·Mar 16, 2022SaveLearn
FEM modeling and simulation of broadband ultrasonic transducers with randomized inhomogeneous backing materialA FEM application for the accurate design of composite backing of ultrasonic transducers is presented. The idea is to obtain the dependence between the volume ratio of the tungsten powder in an epoxy…Eduardo Moreno, Wagner C. A. Pereira, Marco Antonio von Kruger et al.·Mar 16, 2022SaveLearn
Portability: A Necessary Approach for Future Scientific SoftwareToday's world of scientific software for High Energy Physics (HEP) is powered by x86 code, while the future will be much more reliant on accelerators like GPUs and FPGAs. The portable parallelization…Meghna Bhattacharya, Paolo Calafiura, Taylor Childers et al.·Mar 16, 2022SaveLearn
A 3D acoustic propagation model for shallow waters based on an indirect Boundary Element MethodThe purpose of this work is twofold: (a) To present the theoretical formulation of a 3D acoustic propagation model based on a Boundary Element Method (BEM), which uses a half-space Green function in…Edmundo F. Lavia, Juan D. Gonzalez, Silvia Blanc·Mar 15, 2022SaveLearn
DFT-FE 1.0: A massively parallel hybrid CPU-GPU density functional theory code using finite-element discretizationWe present DFT-FE 1.0, building on DFT-FE 0.6 [Comput. Phys. Commun. 246, 106853 (2020)], to conduct fast and accurate large-scale density functional theory (DFT) calculations (reaching ~ 100,000…Sambit Das, Phani Motamarri, Vishal Subramanian et al.·Mar 15, 2022SaveLearn
HEP computing collaborations for the challenges of the next decadeLarge High Energy Physics (HEP) experiments adopted a distributed computing model more than a decade ago. WLCG, the global computing infrastructure for LHC, in partnership with the US Open Science…Simone Campana, Alessandro Di Girolamo, Paul Laycock et al.·Mar 14, 2022SaveLearn
Absence of Walker breakdown in the dynamics of chiral Neel domain walls driven by in-plane strain gradientsWe investigate theoretically the motion of chiral N\'eel domain walls in perpendicularly magnetized systems driven by in-plane strain gradients. We show that such strain drives domain walls…Mouad Fattouhi, Felipe Garcia-Sanchez, Rocio Yanes et al.·Mar 11, 2022SaveLearn
Accurate conservative phase-field method for simulation of two-phase flowsIn this work, we propose a novel phase-field model for the simulation of two-phase flows that is accurate, conservative, bounded, and robust. The proposed model conserves the mass of each of the…Suhas S. Jain·Mar 11, 2022SaveLearn
A High-Order-Accurate 3D Surface Integral Equation Solver for Uniaxial Anisotropic MediaThis paper introduces a high-order accurate surface integral equation method for solving 3D electromagnetic scattering for dielectric objects with uniaxially anisotropic permittivity tensors. The…Jin Hu, Constantine Sideris·Mar 11, 2022SaveLearn
An accurate lattice Boltzmann method for interface capturingAccurately solving phase interface plays a great role in modeling immiscible multiphase flow system. In this letter, we propose an accurate interface-capturing lattice Boltzmann method from the…Hong Liang·Mar 10, 2022SaveLearn
Understanding Electrolyte Filling of Lithium-Ion Battery Electrodes on the Pore Scale Using the Lattice Boltzmann MethodElectrolyte filling is a time-critical step during battery manufacturing that also affects the battery performance. The underlying physical phenomena during filling mainly occur on the pore scale and…Martin P. Lautenschlaeger, Benedikt Prifling, Benjamin Kellers et al.·Mar 10, 2022SaveLearn
Towards Large-Scale and Spatio-temporally Resolved Diagnosis of Electronic Density of States by Deep LearningModern laboratory techniques like ultrafast laser excitation and shock compression can bring matter into highly nonequilibrium states with complex structural transformation, metallization and…Qiyu Zeng, Bo Chen, Xiaoxiang Yu et al.·Mar 9, 2022SaveLearn
A Generic Solution of Fermion Sign ProblemThe fermion sign problem, the biggest obstacle in quantum Monte Carlo calculations, is completely solved in this paper. Here, we find a strategy, in which the contribution from those…J. Wang, D. Y. Sun·Mar 8, 2022SaveLearn
Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiCMachine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning…Yu Xie, Jonathan Vandermause, Senja Ramakers et al.·Mar 8, 2022SaveLearn
A Novel Physics-Regularized Interpretable Machine Learning Model for Grain GrowthExperimental grain growth observations often deviate from grain growth simulations, revealing that the governing rules for grain boundary motion are not fully understood. A novel deep learning model…Weishi Yan, Joseph Melville, Vishal Yadav et al.·Mar 7, 2022SaveLearn