Updating DMD Operators for Changes in Domain PropertiesFast and reliable surrogate models are critical for optimization, control and uncertainty analysis in geological carbon-storage projects, yet high-fidelity multiphase simulators remain too expensive.…Dimitrios Voulanas, Eduardo Gildin·Nov 27, 2025SaveLearn
Single-pixel imaging via data-driven and deep image prior dual networksSingle-pixel imaging(SPI),especially when integrated with deep neural networks like deep image prior networks (DIP-Net) or data-driven networks (DD-Net), has gained considerable attention for its…Jing-yi Shi, Jia-qi Song, Peng-cheng Ji et al.·Nov 27, 2025SaveLearn
A multi-language auto-differentiation module and its application to a parallel particle-in-cell code on distributed computersThe auto differentiable simulation is a type of simulation that outputs of the simulation include not only the simulation result itself, but also their derivatives with respect to various input…Ji Qianga, Yue Hao, Allen Qiang et al.·Nov 26, 2025SaveLearn
Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics SimulationsNumerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of…Tingkai Xue, Chin Chun Ooi, Zhengwei Ge et al.·Nov 26, 2025SaveLearn
Attosecond momentum-resolved resonant inelastic x-ray scattering for imaging coupled electron-hole dynamicsImproving our understanding of electron dynamics is essential for advancing energy transfer, optoelectronics, light harvesting systems and quantum computing. Recent developments in attosecond x-ray…Maksim Radionov, Daria Popova-Gorelova·Nov 25, 2025SaveLearn
Effect of cohesion on the gravity-driven evacuation of metal powder through Triply-Periodic Minimal Surface structuresEvacuating the powder trapped inside the complex cavities of Triply Periodic Minimal Surface (TPMS) structures remains a major challenge in metal-powder-based additive manufacturing. The Discrete…Aashish K Gupta, Christopher Ness, Sina Haeri·Nov 25, 2025SaveLearn
Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular DynamicsSimulating electrified metal/water interfaces with explicit solvent under constant potential is essential for understanding electrochemical processes, yet remains prohibitively expensive with ab…Michele Giovanni Bianchi, Michele Re Fiorentin, Francesca Risplendi et al.·Nov 24, 2025SaveLearn
Fast-Converging and Asymptotic-Preserving DSMCImproving the efficiency of the direct simulation Monte Carlo (DSMC) method has become increasingly urgent with the rapid development of space exploration. To address this issue, the direct…Bin Hu, Liyan Luo, Kaiyuan Wang et al.·Nov 24, 2025SaveLearn
A fast-converging and asymptotic-preserving method for adjoint shape optimization of rarefied gas flowsAdjoint based shape optimization is a powerful technique in fluid-dynamics optimization, capable of identifying an optimal shape within only dozens of design iterations. However, when extended to…Yanbing Zhang, Ruifeng Yuan, Lei Wu·Nov 23, 2025SaveLearn
Generation of Granular Deposition Interfaces using conditional Generative Adversarial Network (cGAN)This work aims at generating 1D interface profiles of granular deposition by a conditional generative adversarial network (cGAN). Our cGAN model employs a U-Net generator and a ResNet discriminator…Seyed Feyzelloh Ghavami Mirmahalle, Seyed Ehsan Nedaaee Oskoee, Maniya Maleki·Nov 23, 2025SaveLearn
A DSMC method for the space homogeneous multispecies Landau equationWe present a Direct Simulation Monte Carlo (DSMC) method for the spatially homogeneous multispecies Landau-Fokker-Planck equation. The scheme is derived from a first-order approximation of the…Andrea Medaglia·Nov 21, 2025SaveLearn
The Solution of Potential-Driven, Steady-State Nonlinear Network Flow Equations via Graph PartitioningThe solution of potential-driven steady-state flow in large networks is required in various engineering applications, such as transport of natural gas or water through pipeline networks. The…Shriram Srinivasan, Kaarthik Sundar·Nov 21, 2025SaveLearn
Deep Learning Framework for Enhanced Neutrino Reconstruction of Single-line Events in the ANTARES TelescopeWe present the N-fit algorithm designed to improve the reconstruction of neutrino events detected by a single line of the ANTARES underwater telescope, usually associated with low energy neutrino…A. Albert, S. Alves, M. André et al.·Nov 20, 2025SaveLearn
A physics-inspired nonlinear momentum method for gradient descent with applications to inverse photonic designIn this work, a nonlinear momentum method is introduced to enhance the convergence performance of momentum-based gradient optimization algorithms. Classical momentum methods, such as the Heavy Ball…Jianing Zhang, Rumei Liu·Nov 20, 2025SaveLearn
Implicit and explicit treatments of model error in numerical simulationNumerical simulations of physical systems exhibit discrepancies arising from unmodeled physics and idealizations, as well as numerical approximation errors stemming from discretization and solver…Danny Smyl·Nov 19, 2025SaveLearn
A Full-Induction Magnetohydrodynamics Solver for Liquid Metal Fusion Blankets in Vertex-CFDMultiphysics modeling of liquid metal fusion blankets, which produce tritium and convert energy of neutrons created via fusion reactions into heat, is crucial for predicting performance, ensuring…Eirik Endeve, Doug Stefanski, Marc-Olivier G. Delchini et al.·Nov 19, 2025SaveLearn
Reconstruction of three-dimensional shapes of normal and disease-related erythrocytes from partial observations using multi-fidelity neural networksReconstruction of 3D erythrocyte or red blood cell (RBC) morphology from partial observations, such as microscope images, is essential for understanding the physiology of RBC aging and the pathology…Haizhou Wen, He Li, Zhen Li·Nov 18, 2025SaveLearn
PAS-Net: Physics-informed Adaptive Scale Deep Operator NetworkNonlinear physical phenomena often show complex multiscale interactions; motivated by the principles of multiscale modeling in scientific computing, we propose PAS-Net, a physics-informed…Changhong Mou, Yeyu Zhang, Xuewen Zhu et al.·Nov 18, 2025SaveLearn
A Self-Adjusting FEM-BEM Coupling Scheme for the Nonlinear Poisson-Boltzmann EquationThe Poisson-Boltzmann equation is widely used to model molecular electrostatics; however, it is usually solved in linearised form because the sinh nonlinearity is challenging, limiting its…Mauricio Guerrero-Montero, Michal Bosy, Christopher D. Cooper·Nov 18, 2025SaveLearn
Statistically controllable microstructure reconstruction framework for heterogeneous materials using sliced-Wasserstein metric and neural networksHeterogeneous porous materials play a crucial role in various engineering systems. Microstructure characterization and reconstruction provide effective means for modeling these materials, which are…Zhenchuan Ma, Qizhi Teng, Pengcheng Yan et al.·Nov 18, 2025SaveLearn
Stability of Extrinsic Cohesive-Zone Model with Penalty-Based Contact in Explicit Dynamic Fragmentation SimulationsDynamic fragmentation simulations are essential for predicting material response at high strain rates, yet explicit dynamic simulations that combine an extrinsic cohesive-zone model (CZM) with…Thibault Ghesquière-Diérickx, Jean-François Molinari, Guillaume Anciaux·Nov 18, 2025SaveLearn
Impacts of Stratospheric Aerosol Injection on Renewable Energy SystemsClimate change is one of the 21st centurys major challenges. However, the progress in reducing greenhouse gas emissions is perceived as being too slow. Hence, more radical technologies such as…Sebastian Kebrich, Luisa Kamp, Jochen Linßen et al.·Nov 17, 2025SaveLearn
Case study of a differentiable heterogeneous multiphysics solver for a nuclear fusion applicationThis work presents a case study of a heterogeneous multiphysics solver from the nuclear fusion domain. At the macroscopic scale, an auto-differentiable ODE solver in JAX computes the evolution of the…Jack B. Coughlin, Archis Joglekar, Jonathan Brodrick et al.·Nov 17, 2025SaveLearn
Scalable learning of macroscopic stochastic dynamicsMacroscopic dynamical descriptions of complex physical systems are crucial for understanding and controlling material behavior. With the growing availability of data and compute, machine learning has…Mengyi Chen, Pengru Huang, Kostya S. Novoselov et al.·Nov 17, 2025SaveLearn
Power law attention biases for molecular transformersTransformers are the go-to architecture for most data modalities due to their scalability. While they have been applied extensively to molecular property prediction, they do not dominate the field as…Jay Shen, Yifeng Tang, Andrew Ferguson·Nov 14, 2025SaveLearn