Hugging Face Trending Papers June 23, 2026

Extended pseudo-spectral physics-informed neural networks for phase-field models

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Phase-field models play a central role in the continuum description of phase separation, in which the bulk free-energy density and the interfacial thickness parameter determine pattern formation and microstructural evolution. In practice, these constitutive quantities are rarely known a priori and must be inferred from limited dynamical observations.

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arXiv Machine Learning

Jun 24

Extended pseudo-spectral physics-informed neural networks for phase-field models

arXiv:2606. 24660v1 Announce Type: cross Abstract: Phase-field models play a central role in the continuum description of phase separation, in which the bulk free-energy density and the interfacial thickness parameter determine pattern formation and microstructural evolution.

By Callum Marsh, Radek Erban, Andreas Munch

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 Machine Learning

Sep 15

Physics-Constrained Neural Surrogate for Domain Growth Prediction in Systems with Conserved Kinetics

The paper introduces a physics-constrained neural network surrogate that learns the microstructural evolution of binary mixtures governed by the Cahn‑Hilliard equation. By imposing conservation of the order parameter as a hard constraint on the network output, the model accurately predicts long‑time phase‑separation dynamics for both critical and off‑critical mixtures, maintaining mixture composition and matching the Lifshitz‑Slyozov domain‑growth law. A variant that enforces conservation only through a penalty term drifts from the initial composition and loses predictive accuracy over long rollouts, underscoring the necessity of the hard constraint for stability.

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

arXiv Machine Learning

4d ago

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

arXiv:2609.36504v1 Announce Type: new Abstract: Accurate prediction of three-dimensional (3D) microstructure evolution remains computationally demanding because high-fidelity phase-field simulations...

By Michael Trimboli, Wenxi Liu, Xianqi Li

efficiency

arXiv Machine Learning

Jun 19

A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling

arXiv:2606. 19378v1 Announce Type: new Abstract: Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge.

By Hyeonbin Moon, Yongjin Choi, Seunghwa Ryu

arXiv Machine Learning

Aug 10

Cascading Through the Hierarchy: Regularizer-Induced Feature Detection as Phase Transitions in Deep Linear Neural Networks

arXiv:2608. 06597v1 Announce Type: cross Abstract: A scientific theory of deep learning, comprising learning dynamics and statistical properties of learned models, is rapidly gaining attention.

By Bj\"orn Ladewig, Ibrahim Talha Ersoy, Karoline Wiesner

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Extended pseudo-spectral physics-informed neural networks for phase-field models

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