mlsanityData to 7 Oct 2026

Machine Learning in Heterogeneous Porous Materials

arXiv 2202.04137 ↗First posted 4 Feb 2022cs.LG cond-mat.mtrl-scicited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 49 of its 192 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

49 of 192 references.

References · 49

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37 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2022Partial Differential Equations Meet Deep Neural Networks: A Surveycited by 25
  2. 2024AI-driven inverse design of materials: Past, present and futurecited by 10
  3. 2023Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolutioncited by 1