arXiv AI June 9, 2026 By Tu Do, Shannon Ryan, Santu Rana

OnlyDense: Reduced-Order Modeling for Lagrangian simulation

arXiv:2606. 09065v1 Announce Type: cross Abstract: In science and engineering, Lagrangian simulation methods such as Smooth Particle Hydrodynamics (SPH) or Material Point Method (MPM) are often employed to study the behavior of dynamic systems.

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arXiv Machine Learning

4d ago

Data-Efficient Time-Dependent PDE Surrogates: Graph Neural Simulators vs. Neural Operators

arXiv:2509.06154v3 Announce Type: replace Abstract: Developing accurate, data-efficient surrogate models is central to advancing AI for Science. Neural operators (NOs), which approximate mappings bet...

By Dibyajyoti Nayak, Somdatta Goswami

benchmarks

arXiv Computer Vision

Aug 25

LagrangeGS: Non-Conservative Lagrangian System on Dynamic 3D Gaussian Splatting

arXiv:2608.22773v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) achieves photorealistic reconstruction of time-varying scenes, and recent physics-aware extensions improve extrapo...

By Shogo Sato, Takuhiro Kaneko, Shoichiro Takeda, Tomoyasu Shimada, Riku Inoue, Kazuhiko Murasaki, Ryuichi Tanida

benchmarks safety

arXiv Machine Learning

Jul 31

Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton

arXiv:2607. 27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable.

By Cooper Jacobus, Beatriz Tucci, Oliver Philcox

arXiv Machine Learning

Jul 22

Hierarchical Physics-Embedded Learning for Partially Known Spatiotemporal Dynamics

arXiv:2510. 25306v3 Announce Type: replace Abstract: Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems.

By Xizhe Wang, Xiaobin Song, Hongbo Zhao, Qingshan Jia, Qianchuan Zhao, Hao Sun, Benben Jiang

rag benchmarks

arXiv AI

Jul 2

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

arXiv:2607. 00460v1 Announce Type: cross Abstract: Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high training costs, error accumulation, and limited generalizability to unseen parameters.

By Xin-Yang Liu, Xiantao Fan, Jian-Xun Wang

rag safety

arXiv Machine Learning

Aug 26

Physics-Integrated Operator Learning via Gaussian Splatting Representations

arXiv:2608.24049v1 Announce Type: new Abstract: Neural operators provide efficient surrogates for spatiotemporal PDE systems, but purely data-driven formulations often accumulate substantial errors d...

By Jihao Zhang, Junyi Guo, Jian-Xun Wang

diffusion benchmarks

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Data-Efficient Time-Dependent PDE Surrogates: Graph Neural Simulators vs. Neural Operators

LagrangeGS: Non-Conservative Lagrangian System on Dynamic 3D Gaussian Splatting

Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton

Hierarchical Physics-Embedded Learning for Partially Known Spatiotemporal Dynamics

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

Physics-Integrated Operator Learning via Gaussian Splatting Representations