About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Training Material Models Using Gradient Descent Algorithms |
Author(s) |
Tianju Chen, Mark Christian Messner |
On-Site Speaker (Planned) |
Tianju Chen |
Abstract Scope |
Oftentimes, calibrating accurate models devolves into the problem of fitting the model parameters against experimental test data. Here, we present the Pyopmat package, an open source framework for calibrating constitutive models against experiment data subjected to various loading conditions using machine learning techniques. The package calculates the exact gradient of the model response with respect to the parameters using a combination of automatic differentiation and the adjoint method. Given this exact gradient, we compare the performance of several gradient-based optimization techniques in fitting realistic constitutive models against data. We demonstrate the efficiency and accuracy of our package through example problems using both synthetic data, generated using known parameter sets, under uniaxial and cyclic loading conditions and also actual high temperature creep-fatigue test data. Besides, Bayesian statistical framework is also embedded in Pyoptmat. The uncertainty of the experiment data are well quantified through utilizing stochastic variational inference. |
Proceedings Inclusion? |
Planned: |
Keywords |
ICME, Machine Learning, Modeling and Simulation |