Introduction to Solid Mechanics and Finite Element Methods
A systematic learning path from solid mechanics fundamentals to finite element methods, covering core concepts and engineering applications.
Updated Oct 13, 2026 📚 3 steps 🏁 0 completed
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入门 Physics-Based Deep Learning (Online Book)
A free online textbook from TU Munich covering physics + deep learning: differentiable physics, PINNs, neural operators and turbulence modeling, with runnable code.
Build foundational intuition for why physics priors are integrated into machine learning
learn 固体力学有限元
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进阶 DeepXDE
The most beginner-friendly PINN framework, with excellent Chinese docs and TensorFlow/PyTorch backends - the top choice to start with physics-informed networks.
Practice solid mechanics PINNs with DeepXDE to solve linear elasticity boundary value problems
tools 固体力学有限元
3
进阶 Fourier Neural Operator (Original Paper)
Introduces neural operators with integral kernels parameterized in Fourier space, enabling resolution-invariant PDE solving - a foundational and highly cited work in physics AI.
Transition from classical FEM to neural operators (FNO), understanding their mathematical connection
papers 固体力学有限元