CM-GAI: Continuum Mechanistic Generative Artificial Intelligence Theory for Data Dynamics
Shan Tang, Ziwei Cao, Zhenling Yang, Jiachen Guo, Yicheng Lu, Wing Kam Liu, Xu Guo

TL;DR
This paper introduces a continuum mechanics-based theoretical framework for generative AI that extends optimal transport theory to model data dynamics, enabling effective data generation with limited data in engineering and beyond.
Contribution
The paper develops a novel continuum mechanics-based theory that generalizes optimal transport for data dynamics, applicable to diverse generative tasks with small data.
Findings
Successfully generates stress-strain responses outside experimental range
Generates temperature-dependent stress fields under thermal loading
Produces plastic strain fields under transient dynamic loading
Abstract
Generative artificial intelligence (GAI) plays a fundamental role in high-impact AI-based systems such as SORA and AlphaFold. Currently, GAI shows limited capability in the specialized domains due to data scarcity. In this paper, we develop a continuum mechanics-based theoretical framework to generalize the optimal transport theory from pure mathematics, which can be used to describe the dynamics of data, realizing the generative tasks with a small amount of data. The developed theory is used to solve three typical problem involved in many mechanical designs and engineering applications: at material level, how to generate the stress-strain response outside the range of experimental conditions based on experimentally measured stress-strain data; at structure level, how to generate the temperature-dependent stress fields under the thermal loading; at system level, how to generate the…
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Taxonomy
TopicsModel Reduction and Neural Networks · Machine Learning in Materials Science · Artificial Intelligence in Healthcare and Education
