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Physics-Informed Deep-Learning for Scientific Computing
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GitHub - lululxvi/deeponet: Learning nonlinear operators via DeepONet
Webb13 apr. 2024 · In particular, the term “physics-informed neural networks” (PINNs) was coined 24 24. M. Raissi, P. Perdikaris, and G. E. Karniadakis, “ Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations ,” J. Comput. Webb一、Introduction PINNs定义:physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. 要介绍pinns,首先要说明它提出的背景。 总的来说,pinns的提出是供科学研究服务的,它的根本目的是解方程,下 … WebbThis module implements the Physics Informed Neural Network (PINN) model for the wave equation. The wave equation is given by (d^2/dt^2 - c^2 d^2/dx^2)u = 0, where c is the wave velocity. We give the initial condition u (t=0, x) = sin (k*x - omega*t) * exp (- (k*x - omega*t)**2) and du (t=0, x)/dt. Dirichlet or Neumann boundary condition is ... bnd 2