|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||A Physics-informed Regularization Approach for Machine Learning Derivation of Constitutive Models
||Karl Garbrecht, Jacob Hochhalter
|On-Site Speaker (Planned)
Machine learning (ML) algorithms have been successfully implemented to derive constitutive relationships for a variety of materials. However, their application has been limited due to concerns over low interpretability, lack of theoretical considerations, and/or poor generalization. A method to incorporate thermodynamic principles and reduce spurious correlations within ML derived constitutive models is presented. The approach utilizes a hyperplasticity formulation based on thermodynamic potentials and a dissipation function. A Gibbs energy potential and dissipation function are optimized to simulated mechanical testing data and subsequent uncertainty analysis of the optimized functions provides a means to establish a physics regularization component within an algorithm's fitness evaluation procedure. Genetic programming based symbolic regression was selected for a proof-of-concept implementation using simulated testing data and is demonstrated with an open-source package, Bingo. However, the method is generally applicable for use in other ML algorithms.
||Machine Learning, Computational Materials Science & Engineering, Other