  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.
