PINN_Simple：探索一维中的PINN实现

# PINN_Simple_ODE_1D From fascinating Idea of PINN (ft. Maziar Raissi), this one is baby step to explore and understand PINNs. Idea is to make PINN for approximating (Exactly) simple 1-D equations and understand the implementation. ## Examples 1. Polynomial - f(x) = y = x^2 in range (-20,20) 2. Trigonometric - f(x) = y = x + sin(4 pi x) in range (0,1) 3. 1st_order_ode - df(x)/dx = 1/x in range(0.5,10) with f(1)=0 ## Citation https://maziarraissi.github.io/PINNs/ @article{raissi2019physics, title={Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations}, author={Raissi, Maziar and Perdikaris, Paris and Karniadakis, George E}, journal={Journal of Computational Physics}, volume={378}, pages={686--707}, year={2019}, publisher={Elsevier} } @article{raissi2017physicsI, title={Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations}, author={Raissi, Maziar and Perdikaris, Paris and Karniadakis, George Em}, journal={arXiv preprint arXiv:1711.10561}, year={2017} } @article{raissi2017physicsII, title={Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations}, author={Raissi, Maziar and Perdikaris, Paris and Karniadakis, George Em}, journal={arXiv preprint arXiv:1711.10566}, year={2017} }