Machine Learning in Heterogeneous Porous Materials
Abstract
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research. Within the scope of ML and materials research, the goal of the workshop was to discuss the state-of-the-art in each community, promote crosstalk and accelerate multi-disciplinary collaborative research, and identify challenges and opportunities. As the end result, four topic areas were identified: ML in predicting materials properties, and discovery and design of novel materials, ML in porous and fractured media and time-dependent phenomena, Multi-scale modeling in heterogeneous porous materials via ML, and Discovery of materials constitutive laws and new governing equations. This workshop was part of the AmeriMech Symposium series sponsored by the National Academies of Sciences, Engineering and Medicine and the U.S. National Committee on Theoretical and Applied Mechanics.
Cite
@article{arxiv.2202.04137,
title = {Machine Learning in Heterogeneous Porous Materials},
author = {Marta D'Elia and Hang Deng and Cedric Fraces and Krishna Garikipati and Lori Graham-Brady and Amanda Howard and George Karniadakis and Vahid Keshavarzzadeh and Robert M. Kirby and Nathan Kutz and Chunhui Li and Xing Liu and Hannah Lu and Pania Newell and Daniel O'Malley and Masa Prodanovic and Gowri Srinivasan and Alexandre Tartakovsky and Daniel M. Tartakovsky and Hamdi Tchelepi and Bozo Vazic and Hari Viswanathan and Hongkyu Yoon and Piotr Zarzycki},
journal= {arXiv preprint arXiv:2202.04137},
year = {2022}
}
Comments
The workshop link is: https://amerimech.mech.utah.edu