Renormalization-Group Geometry of Homeostatically Regulated Reentry NetworksReentrant computation-recursive self-coupling in which a network continuously reinjects and reinterprets its own internal state-plays a central role in biological cognition but remains poorly…Byung Gyu Chae·Dec 22, 2025SaveLearn
Additive general integral equations in thermoelastic micromechanics of compositesThis work presents an enhanced Computational Analytical Micromechanics (CAM) framework for the analysis of linear thermoelastic composite materials (CMs) with random microstructure. The proposed…Valeriy A. Buryachenko·Dec 21, 2025SaveLearn
New RVE concept in thermoelasticity of periodic composites subjected to compact support loadingThis paper introduces an advanced Computational Analytical Micromechanics (CAM) framework for linear thermoelastic composites (CMs) with periodic microstructures. The approach is based on an exact…V. A. Buryachenko·Dec 21, 2025SaveLearn
Long-range electrostatics for machine learning interatomic potentials is easier than we thoughtThe lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar…Dongjin Kim, Bingqing Cheng·Dec 19, 2025SaveLearn
Origins of phase-field crack widening in dynamic fragmentation explainedWe investigate dynamic crack propagation and fragmentation with the phase-field fracture approach. The method was chosen for its ability to yield crack paths that are independent of the underlying…Shad Durussel, Gergely Molnár, Jean-François Molinari·Dec 19, 2025SaveLearn
Spectral finite-element formulation of the optimized effective potential method for atomic structure in the random phase approximationWe present a spectral finite-element formulation of the optimized effective potential (OEP) method for atomic structure calculations in the random phase approximation (RPA). In particular, we develop…Shubhang Krishnakant Trivedi, Phanish Suryanarayana·Dec 19, 2025SaveLearn
Quantitative phase nano-imaging with a laboratory sourceInvestigating the structure of matter at the nanoscale non destructively is a key capability enabled by X-ray imaging. One of the most powerful nano-imaging methods is X-ray ptychography, a coherent…Luca Fardin, Chris Armstrong, Alberto Astolfo et al.·Dec 19, 2025SaveLearn
ClusTEK: A grid clustering algorithm augmented with diffusion imputation and origin-constrained connected-component analysis: Application to polymer crystallizationGrid clustering algorithms are valued for their efficiency in large-scale data analysis but face persistent limitations: parameter sensitivity, loss of structural detail at coarse resolutions, and…Elyar Tourani, Brian J. Edwards, Bamin Khomami·Dec 18, 2025SaveLearn
Sparse Operator-Adapted Wavelet Decomposition Using Polygonal Elements for Multiscale FEM ProblemsWe develop a sparse multiscale operator-adapted wavelet decomposition-based finite element method (FEM) on unstructured polygonal mesh hierarchies obtained via a coarsening procedure. Our approach…Furkan Şık, F. L. Teixeira, B. Shanker·Dec 17, 2025SaveLearn
UGKS and UGKWP Methods for Multiscale Simulation of Electrostatic Plasma in Quasineutral and Hydrodynamic LimitsThis study extends the Unified Gas-Kinetic Scheme (UGKS) and the Unified Gas-Kinetic Wave-Particle (UGKWP) method for electrostatic plasma modeling, ensuring the correct asymptotic limits with…Zhigang Pu, Kun Xu·Dec 17, 2025SaveLearn
Physics-Informed Machine Learning for Two-Phase Moving-Interface and Stefan ProblemsThe Stefan problem is a classical free-boundary problem that models phase-change processes and poses computational challenges due to its moving interface and nonlinear temperature-phase coupling. In…Che-Chia Chang, Te-Sheng Lin, Ming-Chih Lai·Dec 16, 2025SaveLearn
Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimizationArtificial neural networks accurately learn nonlinear, path-dependent material behavior. However, training them typically requires large, diverse datasets, often created via synthetic unit cell…Shunyu Yin, Bernardo P. Ferreira, Gawel Kus et al.·Dec 16, 2025SaveLearn
Generative Monte Carlo Sampling for Constant-Cost Particle TransportWe present Generative Monte Carlo (GMC), a novel paradigm for particle transport simulation that integrates generative artificial intelligence directly into the stochastic solution of the linear…Joseph A. Farmer, Aidan Murray, Johannes Krotz et al.·Dec 16, 2025SaveLearn
HWF-PIKAN: A Multi-Resolution Hybrid Wavelet-Fourier Physics-Informed Kolmogorov-Arnold Network for solving Collisionless Boltzmann EquationPhysics-Informed Neural Networks (PINNs) and more recently Physics-Informed Kolmogorov-Arnold Networks (PIKANs) have emerged as promising approaches for solving partial differential equations (PDEs)…Mohammad E. Heravifard, Kazem Hejranfar·Dec 12, 2025SaveLearn
Stable spectral neural operator for learning stiff PDE systems from limited dataAccurate modeling of spatiotemporal dynamics is crucial to understanding complex phenomena across science and engineering. However, this task faces a fundamental challenge when the governing…Rui Zhang, Han Wan, Yang Liu et al.·Dec 12, 2025SaveLearn
HPRMAT: A high-performance R-matrix solver with GPU acceleration for coupled-channel problems in nuclear physicsI present HPRMAT, a high-performance solver library for the linear systems arising in R-matrix coupled-channel scattering calculations in nuclear physics. Designed as a drop-in replacement for the…Jin Lei·Dec 12, 2025SaveLearn
URANOS -- a novel voxel engine Neutron Transport Monte-Carlo SimulationURANOS is a newly developed 3D neutron transport Monte-Carlo code from thermal to fast energy domains. It was originally developed for the CASCADE detector. The purpose of this simulation program is…Markus Köhli, Martin Schrön, Steffen Zacharias et al.·Dec 11, 2025SaveLearn
MULE -- A Co-Generation Fission Power Plant Concept to Support Lunar In-Situ Resource UtilisationFor a sustained human presence on the Moon, robust in-situ resource utilisation supply chains to provide consumables and propellant are necessary. A promising process is molten salt electrolysis,…Julius Mercz, Philipp Reiss, Christian Reiter·Dec 11, 2025SaveLearn
Ultra-Fast Muon Transport via Histogram Sampling on GPUsWe present a GPU-accelerated method for muon transport based on histogram sampling that delivers orders of magnitude faster performance than CPU-based Geant4 simulation. Our method employs…Luis Felipe P. Cattelan, Shah Rukh Qasim, Patrick H. Owen et al.·Dec 11, 2025SaveLearn
Generative Modeling of Entangled Polymers with a Distance-Based Variational AutoencoderWe present a variational autoencoder framework for learning and generating configurations of structured polymer globules from distance matrices. We used coarse-grained molecular dynamics to sample…Pietro Chiarantoni, Oscar Serra, Mohammad Erfan Mowlaei et al.·Dec 10, 2025SaveLearn
A Model-Guided Neural Network Method for the Inverse Scattering ProblemInverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) methods offer increased…Olivia Tsang, Owen Melia, Vasileios Charisopoulos et al.·Dec 10, 2025SaveLearn
Matrix-free algorithms for fast ab initio calculations on distributed CPU architectures using finite-element discretizationFinite-element (FE) discretisations have emerged as a powerful real-space alternative to large-scale Kohn-Sham density functional theory (DFT) calculations, offering systematic convergence, excellent…Gourab Panigrahi, Phani Motamarri·Dec 9, 2025SaveLearn
Seasonal thermal stress analysis of defective mass concrete sidewalls based on the average forming temperature methodThermal cracking in urban underground sidewalls is frequently observed when structures are cast in summer and enter service in winter, as seasonal temperature gradients act under structural…Ziyan Zhao, Ting Peng, Peng Wu et al.·Dec 9, 2025SaveLearn
Conservative adaptive-precision interatomic potentialsAdaptive precision molecular dynamics simulations have developed along energy- and force-coupling approaches, which allow for a continuous transition between different particle descriptions or…David Immel, Ralf Drautz, Godehard Sutmann·Dec 8, 2025SaveLearn
Optimized Machine Learning Methods for Studying the Thermodynamic Behavior of Complex Spin SystemsThis paper presents a systematic study of the application of convolutional neural networks (CNNs) as an efficient and versatile tool for the analysis of critical and low-temperature phase states in…Dmitrii Kapitan, Pavel Ovchinnikov, Konstantin Soldatov et al.·Dec 8, 2025SaveLearn