GPU Acceleration of Collinear and Noncollinear DFT Using a Numerical Atomic Orbital-Based DFT CodeWe implement GPU acceleration of collinear and noncollinear density functional theory (DFT) calculations in the numerical atomic orbitals (NAOs) code OpenMX by offloading matrix multiplications and…Hiroyuki Kawai, Takuya Sekikawa, Taisuke Ozaki et al.·Jun 8, 2026SaveLearn
AutoPot: Automated and massively parallelized construction of Machine-Learning PotentialsMachine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is…Max Hodapp, Guillaume Anciaux·Jun 8, 2026SaveLearn
Stochastic Density Functional Theory Through the Lens of Multilevel Monte Carlo MethodThe stochastic density functional theory (sDFT) has exhibited advantages over the standard Kohn-Sham DFT method and has become an attractive approach for large-scale electronic structure…Xue Quan, Huajie Chen·Jun 8, 2026SaveLearn
Computer-Assisted Proofs for Geometric Optimization: From Crystallization to Carbon NanotubesWe present a framework based on computer-assisted proofs that turns geometry optimization simulations for atomistic structures into mathematical proofs. Starting from a numerically computed…Miguel Ayala, Rustum Choksi, Benedikt Wirth·Jun 7, 2026SaveLearn
Total-Lagrangian vectorial lattice Boltzmann method for finite-strain hyperelasticity with curved boundariesFinite-strain hyperelasticity on curved embedded domains poses a geometric challenge for lattice Boltzmann methods. After streaming across an embedded material surface, the missing population is…Jingsen Feng, Xu Chu·Jun 6, 2026SaveLearn
Acquiring Human-Like Data-Efficient Mechanics Prediction from Deep Reinforcement LearningHumans can infer mechanical outcomes by learning from a few observations. This capacity for mechanics intuition is acquired in a data-efficient manner. Here, we propose a reinforcement learning…Jingruo Peng, Shuze Zhu·Jun 6, 2026SaveLearn
Electromagnetic Characterization of Magnetic Ring: Case of Circular Cross-Section ShapeThis paper introduces a comprehensive two-dimensional analytical model of a toroidal magnetic ring with circular cross-section under sinusoidal excitation. Applying Maxwell's equations in local…Taha El Hajji, Lars Sjöberg·Jun 5, 2026SaveLearn
Consensus-based adaptive sampling and approximation for high-dimensional energy landscapesWe present a consensus-based framework that unifies phase space exploration with posterior-residual-based adaptive sampling for surrogate construction in high-dimensional energy landscapes. Unlike…Liyao Lyu, Huan Lei·Jun 5, 2026SaveLearn
Tensor network compression using fluid dynamics as a testbed: Analytical foundations in one dimensionHigh performance computers produce extreme-scale data sets that require sampling or compression if they are to be used to their full potential. Existing data compression techniques typically exploit…Matthew D. Horner, Callum W. Duncan, Oliver T. Brown et al.·Jun 4, 2026SaveLearn
GEMINI: Generalized Ensnarlment Measure from Incomplete-linkage of Network-network InteractionsSpatially embedded networks are central to many physical and biological systems, where geometry and connectivity jointly shape structure and function. Examples abound across the scales of biological…Yu Tian, Chinmayi Subramanya, Carl D. Modes·Jun 3, 2026SaveLearn
Size and solvent effects on cellulose transverse anisotropy and toughening designCellulose nanocrystals (CNCs) are a promising class of materials derived from the most abundant natural polymer resource on Earth. Hydroxyl-induced polarity is a crucial advantage of CNCs, making…Xu Dong·Jun 3, 2026SaveLearn
Sparse and low-rank kinetic distribution estimationIn this paper, we consider methods that allow for memory-efficient storage of high-dimensional distributions and retain certain key features thereof, specifically in a kinetic theory context. We…Georgii Oblapenko, Lambert Theisen, Rostislav-Paul Wilhelm et al.·Jun 3, 2026SaveLearn
Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent SensingReal-time monitoring of the temperature distribution within components and sub-structures is a challenging topic in many systems due to restrictions on feasible sensor locations. While machine…Monika Stipsitz, Hèlios Sanchis-Alepuz, Jacob Reynvaan et al.·Jun 3, 2026SaveLearn
Factorizing binary tensors into quantics tensor trainsThe conversion of functions to quantics tensor trains is a well-established procedure and can either be done analytically or numerically. Numerical conversion schemes are based on singular value…Paul Haubenwallner, Matthias Heller·Jun 3, 2026SaveLearn
ATLAS-NN: Adaptive Transfer Learnable Symplectic-aware Neural Network for Long-Time Hamiltonian DynamicsModeling Hamiltonian systems over long temporal intervals remains a significant challenge due to intrinsic multiscale structures and rapid nonlinear transitions. While Hamiltonian Neural Networks…Changhong Mou, Dinghua Xu, Xiyue Zuo et al.·Jun 3, 2026SaveLearn
Gradient estimators for parameter inference in discrete stochastic kinetic modelsStochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. For deterministic models, parameter inference often relies on…Ludwig Burger, Annalena Kofler, Lukas Heinrich et al.·Jun 3, 2026SaveLearn
Flow-priority optimization of additively manufactured variable-TPMS lattice heat exchanger based on macroscopic analysisHeat exchangers incorporating triply periodic minimal surface (TPMS) lattice structures have attracted considerable research interest because they promote uniform flow distribution, disrupt boundary…Kazutaka Yanagihara, Jun Iwasaki, Kiyoto Saso et al.·Jun 3, 2026SaveLearn
Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF ImplosionsContinued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input parameters, followed by design optimization. However,…Tyler E. Maltba, Ben S. Southworth, Jeffrey R. Haack et al.·Jun 3, 2026SaveLearn
A Differentiable Framework for Full and Phaseless Data Inversion Using Neural Implicit Contrast-Source RepresentationIn this study, we extend the contrast source inversion to a fully differentiable, unsupervised framework based on a neural implicit representation of the contrast source. Specifically, instead of a…Haoran Sun, Daoqi Liu, Hongyu Zhou et al.·Jun 3, 2026SaveLearn
The influence of volumetric shrinkage on the metal solidification process under localized energy depositionAccurate simulation of metal melting and solidification under localized energy deposition is crucial for the advancement of beam-based manufacturing technologies. This study presents an extended…Daniil V. Panov, Oleg A. Rogozin, Oleg V. Vasilyev·Jun 2, 2026SaveLearn
TransportBench: A Comprehensive Benchmark for Non-Equilibrium Flow TransportScientific machine learning models, as versatile tools for numerical simulation and analysis, are increasingly transforming the landscape of fluid mechanics research. However, existing datasets and…Xu Wang, Minghao Li, Qizhen Hong et al.·Jun 2, 2026SaveLearn
An efficient and energy stable framework for phase field simulations of grain growth in additive manufacturingPhase field simulations play a key role in the understanding of microstructure evolution in additive manufacturing. However, they have been found extremely computationally expensive. One of the…Chaoqian Yuan, Chinnapat Panwisawas, Ye Lu·Jun 2, 2026SaveLearn
Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D FlameTurbulent combustion simulations are crucial for many scientific and engineering systems. However, the high cost to fully resolve the complex multiscale and multiphysics behavior makes direct…Nicolas J. Tricard, Benjamin C. Koenig, Sili Deng·Jun 1, 2026SaveLearn
Sequential Subspace Mode Adaptation for the Reduced-Order Homogenization of Dissipative Microstructures using E3C Hyper-ReductionThree-dimensional inelastic computational homogenization of complex engineering components requires a multitude of nonlinear microstructural simulations, making it computationally expensive. This…Hauke Goldbeck, Stephan Wulfinghoff·Jun 1, 2026SaveLearn
Differentiable Particle-Mesh Ewald with Cartesian Tensor Message Passing for Learning Long-Range Electrostatics and Dipole ResponseMachine learning interatomic potentials (MLIPs) can approach quantum accuracy for short-range chemistry, but most architectures remain local and fail to capture the long-range electrostatic and…Zhiyue Guo, Junjie Wang, Haoting Zhang et al.·Jun 1, 2026SaveLearn