Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials

29 July 2024

Papers citing "Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials"

6 / 6 papers shown

Title
Machine Learning for Condensed Matter Physics Edwin Bedolla L. C. Padierna R. Castañeda-Priego AI4CE 45 67 0 28 May 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 310 42,038 0 03 Dec 2019
A high-bias, low-variance introduction to Machine Learning for physicists Pankaj Mehta Marin Bukov Ching-Hao Wang A. G. Day C. Richardson Charles K. Fisher D. Schwab AI4CE 74 870 0 23 Mar 2018
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 508 7,952 0 13 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.1K 149,474 0 22 Dec 2014
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling Junyoung Chung Çağlar Gülçehre Kyunghyun Cho Yoshua Bengio 361 12,662 0 11 Dec 2014