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Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics

Engineering and Technology

Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics

W. He, J. Li, et al.

Introducing the ml-PINN framework, a groundbreaking approach by Weiwei He, Jinzhao Li, Xuan Kong, and Lu Deng for solving complex fourth-order PDEs in computational structural mechanics. This innovative method enhances accuracy and speed beyond classical PINNs, paving the way for real-time simulations in digital twin systems.

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