A parallel-in-time method based on the Parareal algorithm and High-Order Dynamic Mode Decomposition with applications to fluid simulationsThe high cost of sequential time integration is one major constraint that limits the speedup of a time-parallel algorithm like the Parareal algorithm due to the difficulty of coarsening time steps in…Weifan Liu·Mar 5, 2025SaveLearn
Learning finite symmetry groups of dynamical systems via equivariance detectionIn this work, we introduce the Equivariance Seeker Model (ESM), a data-driven method for discovering the underlying finite equivariant symmetry group of an arbitrary function. ESM achieves this by…Pablo Calvo-Barlés, Sergio G. Rodrigo, Luis Martín-Moreno·Mar 4, 2025SaveLearn
FGM Modeling of Thermo-Diffusive Unstable Lean Premixed Hydrogen-Air FlamesUltra-lean premixed hydrogen combustion is a possible solution to decarbonize industry, while limiting flame temperatures and thus nitrous oxide emissions. These lean hydrogen/air flames experience…Stijn N. J. Schepers, Jeroen A. van Oijen·Mar 4, 2025SaveLearn
Hybrid Quantum Physics-informed Neural Network: Towards Efficient Learning of High-speed FlowsThis study benchmarks hybrid quantum physics-informed neural network (HQPINN) to model high-speed flows, compared against classical physics-informed neural networks (PINNs) and fully quantum neural…Fong Yew Leong, Wei-Bin Ewe, Tran Si Bui Quang et al.·Mar 4, 2025SaveLearn
Reconstruction of proton relative stopping power with a granular calorimeter detector modelProton computed tomography (pCT) aims to facilitate precise dose planning for hadron therapy, a promising and effective method for cancer treatment. Hadron therapy utilizes protons and heavy ions to…M. Aehle, J. Alme, G. G. Barnaföldi et al.·Mar 4, 2025SaveLearn
Negative Weights and Weight Cancellation to Treat Anisotropic Scattering in Multigroup Monte Carlo SimulationsThe Monte Carlo method is typically considered the gold standard for simulating reactor physics problems, as it does not require discretization of the phase space. This is not necessarily true though…Parth Singh, Hunter Belanger·Mar 3, 2025SaveLearn
Can machines learn density functionals? Past, present, and future of ML in DFTDensity functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern…Ryosuke Akashi, Mihira Sogal, Kieron Burke·Mar 3, 2025SaveLearn
Entropic learning enables skilful forecasts of ENSO phase at up to two years lead timeThis paper extends previous work (Groom et al., Artif. Intell. Earth Syst., 2024) in applying the entropy-optimal Sparse Probabilistic Approximation (eSPA) algorithm to predict ENSO phase,…Michael Groom, Davide Bassetti, Illia Horenko et al.·Mar 3, 2025SaveLearn
Insights into dendritic growth mechanisms in batteries: A combined machine learning and computational studyIn recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low cost, and…Zirui Zhao, Junchao Xia, Si Wu et al.·Mar 2, 2025SaveLearn
Stress, Strain, or Displacement? A Novel Machine Learning Based Framework to Predict Mixed Mode I/II Fracture ToughnessAccurate prediction of fracture toughness under complex loading conditions, like mixed mode I/II, is essential for reliable failure assessment. This paper aims to develop a machine learning framework…Amir Mohammad Mirzaei·Mar 2, 2025SaveLearn
One-Cell Inversion for Solving Higher-Order Time-Dependent Radiation Transport on GPUsTo find deterministic solutions to the transient discrete-ordinates neutron-transport equation, source iterations (SI) are typically used to lag the scattering (and fission) source terms from…Joanna Piper Morgan, Ilham Variansyah, Todd S. Palmer et al.·Mar 1, 2025SaveLearn
Data Assimilation With An Integral-Form Ensemble Square-Root FilterGeoscientific applications of ensemble Kalman filters face several computational challenges arising from the high dimensionality of the forecast covariance matrix, particularly when this matrix…Robin Armstrong, Ian Grooms·Mar 1, 2025SaveLearn
Particle Trajectory Prediction in Discrete Element Simulations using a Graph-Based Interaction-Aware ModelThis study explores the applicability of a graph-based interaction-aware trajectory prediction model, originally developed for the transportation domain, to forecast particle trajectories in…Abhishek Setty, Lukas Morand, Poojitha Ramachandra et al.·Feb 28, 2025SaveLearn
Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular SimulationsSimulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial regions. In this…Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr·Feb 28, 2025SaveLearn
Picosecond-scale Heterogeneous Melting of Metals at Extreme Non-equilibrium StatesExtreme electron-ion non-equilibrium states, generated by ultrafast laser excitation, lead to melting processes that are fundamentally different from those under conventional thermal equilibrium and…Qiyu Zeng, Xiaoxiang Yu, Bo Chen et al.·Feb 28, 2025SaveLearn
5f Electron Induced Spin Transport by Sandwich-Type PhthalocyanineIn this study, we employed the non-equilibrium Green's function method combined with density functional theory to investigate the spin transport properties of the actinide sandwich phthalocyanine…Lu Xu, Ding Wang, Xiaobo Yuan et al.·Feb 28, 2025SaveLearn
Efficient solution strategy to couple micromagnetic simulations with ballistic transport in magnetic tunnel junctionsWe present a computationally efficient strategy that allows to simulate magnetization switching driven by spin-transfer torque in magnetic tunnel junctions within a micromagnetic model coupled with a…Peter Flauger, Claas Abert, Dieter Suess·Feb 27, 2025SaveLearn
A finite element approach for modelling the fracture behaviour of unidirectional FFF-printed partsWe present a finite element modelling approach for unidirectional Fused Filament Fabrication (FFF)-printed specimens under tensile loading. In this study, the focus is on the fracture behaviour, the…Simon Seibel, Josef Kiendl·Feb 27, 2025SaveLearn
Mixed Finite Element Analysis of Flexoelectric Response: Exploring Unit Cell Stacking and Strain Gradient ModulationFlexoelectricity, a coupling between strain gradients and electric polarization, has attracted significant interest due to its critical role in enhanced effects at small scales and its applicability…Arash Kazemi, Kshiteej J Deshmukh, Susan Trolier-McKinstry et al.·Feb 26, 2025SaveLearn
PySEMTools: A library for post-processing hexahedral spectral element dataPySEMTools is a Python-based library for post-processing simulation data produced with high-order hexahedral elements in the context of the spectral element method in computational fluid dynamics. It…Adalberto Perez, Siavash Toosi, Tim Felle Olsen et al.·Feb 26, 2025SaveLearn
Massive-Scale Simulations of 2D Ising and Blume-Capel Models on Rack-Scale Multi-GPU SystemsWe present high-performance implementations of the two-dimensional Ising and Blume-Capel models for large-scale, multi-GPU simulations. Our approach takes full advantage of the NVIDIA GB200 NVL72…Mauro Bisson, Massimo Bernaschi, Massimiliano Fatica et al.·Feb 25, 2025SaveLearn
Learning atomic forces from uncertainty-calibrated adversarial attacksAdversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials…Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson et al.·Feb 25, 2025SaveLearn
Physics- and data-driven Active Learning of neural network representations for free energy functions of materials from statistical mechanicsAccurate free energy representations are crucial for understanding phase dynamics in materials. We employ a scale-bridging approach to incorporate atomistic information into our free energy model by…Jamie Holber, Krishna Garikipati·Feb 25, 2025SaveLearn
Design of resilient structures by randomization and bistabilityThis paper examines various ways of improving the impact resilience of protective structures. Such structures' purpose is to dissipate an impact's energy while avoiding cracking and failure. We have…Debdeep Bhattacharya, Tyler P. Evans, Andrej Cherkaev·Feb 24, 2025SaveLearn
Renormalization-Inspired Effective Field Neural Networks for Scalable Modeling of Classical and Quantum Many-Body SystemsWe introduce Effective Field Neural Networks (EFNNs), a new architecture based on continued functions -- mathematical tools used in renormalization to handle divergent perturbative series. Our key…Xi Liu, Yujun Zhao, Chun Yu Wan et al.·Feb 24, 2025SaveLearn