Basic stability tests of machine learning potentials for molecular simulations in computational drug discoveryNeural network potentials trained on quantum-mechanical data can calculate molecular interactions with relatively high speed and accuracy. However, neural network potentials might exhibit…Kavindri Ranasinghe, Adam L. Baskerville, Geoffrey P. F. Wood et al.·Mar 14, 2025SaveLearn
Fully GPU-Accelerated, Matrix-Free Immersed Boundary Method for Complex Fiber-reinforced Hyperelastic Cardiac ModelsThe immersed boundary (IB) method has become a leading approach in cardiac fluid-structure interaction (FSI) modeling due to its ability to handle large deformations and complex geometries without…Pengfei Ma, Li Cai, Xuan Wang et al.·Mar 14, 2025SaveLearn
Utilizing rate-independent hysteresis for analog computingPhysical systems exhibiting hysteresis are increasingly being used in neuromorphic and in-memory computing research. Generally, the resistance switching of devices with rate-independent hysteresis…Lina Jaurigue, Kathy Lüdge·Mar 14, 2025SaveLearn
Fourier Neural Operator based surrogates for CO2 storage in realistic geologiesThis study aims to develop surrogate models for accelerating decision making processes associated with carbon capture and storage (CCS) technologies. Selection of sub-surface CO2 storage sites…Anirban Chandra, Marius Koch, Suraj Pawar et al.·Mar 14, 2025SaveLearn
Multiscale simulation of interacting turbulent and rarefied gas flows in the DSMC frameworkA multiscale stochastic-deterministic coupling method is proposed to investigate the complex interactions between turbulent and rarefied gas flows within a unified framework. This method…Liyan Luo, Songyan Tian, Lei Wu·Mar 13, 2025SaveLearn
KARL -- A Monte Carlo model for atomic and molecular processes in the tritium atmosphere of the KATRIN experimentA new parallelized simulation code is presented, which uses a Monte Carlo method to determine particle spectra in the KATRIN source. Reaction chains are generated from the decay of tritium within the…Christian Sendlinger, Jonas Kellerer, Felix Spanier·Mar 13, 2025SaveLearn
Modelling lined rock caverns subject to hydrogen embrittlement and cyclic pressurisation in fractured rock massesThe technology of lined rock cavern (LRC) with great geographical flexibility is a promising, cost-effective solution to underground hydrogen storage. However, the air-tight steel tanks used in this…Chenxi Zhao, Haiyang Yu, Zixin Zhang et al.·Mar 12, 2025SaveLearn
JENA Computing Initiative WP2 Report: Software and Heterogeneous ArchitecturesThe scientific communities of nuclear, particle, and astroparticle physics are continuing to advance and are facing unprecedented software challenges due to growing data volumes, complex computing…Mohammad Al-Turany, David Chamont, Davide Costanzo et al.·Mar 12, 2025SaveLearn
The pseudo-analytical density solution to parameterized Fokker-Planck equations via deep learningEfficiently solving the Fokker-Planck equation (FPE) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solutions or density…Xiaolong Wang, Jing Feng, Gege Wang et al.·Mar 12, 2025SaveLearn
The Alamo multiphysics solver for phase field simulations with strong-form mechanics and block structured adaptive mesh refinementAlamo is a high-performance scientific code that uses block-structured adaptive mesh refinement to solve such problems as: the ignition and burn of solid rocket propellant, plasticity, damage and…Brandon Runnels, Vinamra Agrawal, Maycon Meier·Mar 11, 2025SaveLearn
Fast, Accurate Numerical Evaluation of Incomplete Planck IntegralsMethods for computing the integral of the Planck blackbody function over a finite spectral range, the so-called incomplete Planck integral, are necessary to perform multigroup radiative transfer…Whit Lewis, Ryan G. McClarren·Mar 11, 2025SaveLearn
Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics?Machine learning force fields have emerged as promising tools for molecular dynamics (MD) simulations, potentially offering quantum-mechanical accuracy with the efficiency of classical MD. Inspired…Denan Li, Jiyuan Yang, Xiangkai Chen et al.·Mar 11, 2025SaveLearn
Physics-based AI methodology for Material Parameter Extraction from Optical DataWe report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization…M. Koumans, J. L. M. van Mechelen·Mar 11, 2025SaveLearn
A quantum Monte Carlo algorithm for arbitrary high-spin HamiltoniansWe present a universal quantum Monte Carlo algorithm for simulating arbitrary high-spin (spin greater than 1/2) Hamiltonians, based on the recently developed permutation matrix representation (PMR)…Arman Babakhani, Lev Barash, Itay Hen·Mar 11, 2025SaveLearn
Wave-Particle Based Multiscale Modeling and Simulation of Non-equilibrium Turbulent FlowsThis paper presents a novel methodology for the direct numerical modeling and simulation of turbulent flows. The kinetic model equation is firstly extended to turbulent flow with the account of…Xiaojian Yang, Kun Xu·Mar 10, 2025SaveLearn
Global physics-informed neural networks (GPINNs): from local point-wise constraint to global nodal associationRecently, physics-informed neural networks (PINNs) and their variants have gained significant popularity as a scientific computing method for solving partial differential equations (PDEs), whereas…Feng Chen, Yiran Meng, Kegan Li et al.·Mar 9, 2025SaveLearn
An implicit shock tracking method for simulation of shock-dominated flows over complex domains using mesh-based parametrizationsA mesh-based parametrization is a parametrization of a geometric object that is defined solely from a mesh of the object, e.g., without an analytical expression or computer-aided design (CAD)…Alexander M. Perez Reyes, Matthew J. Zahr·Mar 7, 2025SaveLearn
Many-Body Vertex Effects: Time-Dependent Interaction Kernel with Correlated Multi-Excitons in the Bethe-Salpeter EquationBuilding on a beyond-GW many-body framework that incorporates higher-order vertex effects in the self-energy -- giving rise to T-matrix and second-order exchange contributions -- this approach is…Brian Cunningham·Mar 7, 2025SaveLearn
Quantum generative adversarial networks for gluon initiated jets generationQuantum computing has the potential to offer significant advantages over classical computing, making it a promising avenue for exploring alternative methods in High Energy Physics (HEP) simulations.…Rey Guadarrama, Sergei Gleyzer, Mariia Baidachna et al.·Mar 6, 2025SaveLearn
Multiscale Analysis of Woven Composites Using Hierarchical Physically Recurrent Neural NetworksMultiscale homogenization of woven composites requires detailed micromechanical evaluations, leading to high computational costs. Data-driven surrogate models based on neural networks address this…Ehsan Ghane, Marina A. Maia, Iuri B. C. M. Rocha et al.·Mar 6, 2025SaveLearn
Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene SynthesisMachine learning in materials science faces challenges due to limited experimental data, as generating synthesis data is costly and time-consuming, especially with in-house experiments. Mining data…Devi Dutta Biswajeet, Sara Kadkhodaei·Mar 6, 2025SaveLearn
Gradient-enhanced PINN with residual unit for studying forward-inverse problems of variable coefficient equationsPhysics-informed neural network (PINN) is a powerful emerging method for studying forward-inverse problems of partial differential equations (PDEs), even from limited sample data. Variable…Hui-Juan Zhou, Yong Chen·Mar 6, 2025SaveLearn
Code-Verification Techniques for an Arbitrary-Depth Electromagnetic Slot ModelElectromagnetic slot models are employed to efficiently simulate electromagnetic penetration through openings in an otherwise closed electromagnetic scatterer. Such models, which incorporate varying…Brian A. Freno, Neil R. Matula, Robert A. Pfeiffer et al.·Mar 6, 2025SaveLearn
Weighted balanced truncation method for approximating kernel functions by exponentialsKernel approximation with exponentials is useful in many problems with convolution quadrature and particle interactions such as integral-differential equations, molecular dynamics and machine…Yuanshen Lin, Zhenli Xu, Yusu Zhang et al.·Mar 5, 2025SaveLearn
Ab Initio Mechanisms and Design Principles for Photodesorption from TiO2Photocatalytic reactions often exhibit fast kinetics and high product selectivity, qualities which are desirable but difficult to achieve simultaneously in thermally driven processes. However,…Aaron R. Altman, Felipe H. da Jornada·Mar 5, 2025SaveLearn