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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Physics-Informed Constitutive Calibration as a Data Problem: From Quasi-Static-to-High-Rate Experiments and FEA Validation
Author(s) Erwin Cazares, Edgar O Tarin, Brian E Schuster
On-Site Speaker (Planned) Erwin Cazares
Abstract Scope Calibrating a flow-stress model from mechanical tests is a data-centric problem. A campaign spanning quasi-static to high-rate loading yields only a few dozen stress-strain curves. With sparse data, the classical least-squares approach cannot separate correlated parameters, says nothing about the response between tested rates, and estimates uncertainty only after the fit. We present a physics-informed calibration architecture that co-trains a flexible neural surrogate with the constrained constitutive law. The two are coupled to measured data and to collocation points where no test was run, and an ensemble of independent fits reports parameter uncertainty. A model-agnostic core treats each law as data, so two laws of contrasting origin calibrate through one pipeline: the empirical Johnson-Cook law and the dislocation-based Zerilli-Armstrong law in its distinct FCC and BCC forms. We validate the material parameters using finite element methods by comparing the simulated specimen response with measured curves across the full rate range.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Modeling and Simulation, Other

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