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About A Deep Learning Framework For Solution and Discovery in Solid Mechanics
This document presents a deep learning framework called Physics-Informed Neural Networks (PINN) for solving problems in solid mechanics. PINN incorporates governing equations like momentum balance and constitutive relations into neural networks. The framework is applied to linear elasticity problems and extended to nonlinear elastoplasticity. PINN uses multiple neural networks for more accurate solutions and is validated on synthetic data from finite element methods. PINN converges faster on higher-order data and can accurately predict solutions for new parameter values after training on a sparse dataset, showing potential for sensitivity analysis.
Detailed Information
| Author: | ['eliwz'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 24 |
| Language: | English |
| Format: | |
| Price: | FREE |
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