arXiv Machine Learning September 30, 2026 By Michael Trimboli, Wenxi Liu, Xianqi Li

Efficient and Scalable Physics-Guided Fully Convolutional Spatiotemporal Learning for 3D Microstructure Evolution Prediction

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

efficiency

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

Jun 26

Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics

arXiv:2606. 26128v1 Announce Type: new Abstract: The spatiotemporal evolution of many physical, chemical, and biological systems is described by nonlinear partial differential equations (PDEs).

By Vijay Yadav, Madhu Priya, Manish Dev Shrimali, Prabhat K. Jaiswal

arXiv AI

Jun 17

ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics

arXiv:2606. 17668v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulation is computationally demanding, particularly for large-scale systems requiring long-term analysis.

By Kexin Wu, Luonan Chen, Renxiao Wang

llms benchmarks

arXiv Machine Learning

Jun 2

Flowers: A Warp Drive for Neural PDE Solvers

arXiv:2603. 04430v2 Announce Type: replace Abstract: We introduce Flowers, a neural architecture for learning PDE solution operators built entirely from multihead warps.

By Till Muser, Alexandra Spitzer, Matti Lassas, Maarten V. de Hoop, Ivan Dokmani\'c

llms benchmarks

arXiv Machine Learning

1d ago

Learning Macroscopic Dynamics without Reconstructing Microscopic States

arXiv:2609.37392v1 Announce Type: new Abstract: Modeling the temporal evolution of macroscopic properties of complex systems is an important scientific task. To predict this evolution without full mi...

By Zhichao Han, Yue Zhao, Qianxiao Li

arXiv Machine Learning

Jul 7

A Physics-Regulated Neural Framework for Learning 3D Grain Growth Dynamics

arXiv:2607. 04680v1 Announce Type: new Abstract: Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws.

By Zhihui Tian, Kang Yang, Michael Tonks, Amanda R. Krause, Joel B. Harley

One email a morning, machine-written

Related stories

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

Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics

ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics

Flowers: A Warp Drive for Neural PDE Solvers

Learning Macroscopic Dynamics without Reconstructing Microscopic States

A Physics-Regulated Neural Framework for Learning 3D Grain Growth Dynamics