非均匀多孔材料中的机器学习
Machine Learning in Heterogeneous Porous Materials
摘要：
“异质多孔材料中的机器学习研讨会”汇集了应用数学、多孔介质和材料科学的国际科学界以及异质材料、机器学习（ML）和应用数学领域的专家，以确定ML如何推动材料研究。在ML和材料研究的范围内，研讨会的目标是讨论每个社区的最新技术，促进串扰和加速多学科合作研究，并确定挑战和机遇。作为最终结果，确定了四个主题领域：预测材料特性的ML，以及新材料的发现和设计，多孔和断裂介质中的ML以及时间相关现象，通过ML在非均质多孔材料中的多尺度建模，以及材料本构关系和新控制方程的发现。这次研讨会是由美国国家科学院、工程院和医学院以及美国国家理论和应用力学委员会主办的AmeriMech系列研讨会的一部分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
