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Modular machine learning-based elastoplasticity: generalization in the context of limited data
The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics.
Calibrating constitutive models with full-field data via physics informed neural networks
The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation.
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