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Physics-informed Deep Neural Operators for Solid Mechanics
Recently, physics-informed neural operators (PINOs) have been introduced as a new approach for solving complex problems in engineering, by combining data with knowledge of the underlying governing equations. The concept is an extension of previously successful purely data-driven deep neural operators. In this project, the student will explore the application of PINOs on solid mechanics problems, with the goal of simulating the behavior of materials under various loading conditions. Applications will be considered in the context of geotechnical or structural engineering. The generalization capabilities of the method will be evaluated, and its accuracy will be compared to conventional numerical solutions. The findings aim to advance computational tools for engineering design and analysis, bridging the gap between traditional numerical methods and scientific machine learning.
pino-for-solid-mechanics.pdf
