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A physics-informed GAN framework based on model-free data-driven computational mechanics
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DOI：10.1016/j.cma.2024.116907.png)
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Abstract
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Model -free data -driven computational mechanics, first proposed by Kirchdoerfer and Ortiz, replace phenomenological models with numerical simulations based on sample datasets in strain-stress space. In this study, we integrate this paradigm within physics -informed generative adversarial networks (GANs). We enhance the conventional physics -informed neural network framework by implementing the principles of data -driven computational mechanics into GANs. Specifically, the generator is informed by physical constraints, while the discriminator utilizes the closest strain-stress data to discern the authenticity of the generator's output. This combined approach presents a new formalism to harness data -driven mechanics and deep learning to simulate and predict mechanical behaviors.
Keywords:
Model-free data-driven
Generative adversarial networks
Data-driven computing
Physics-informed neural networks
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