rmojgani / LPINNs
To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary cond…
☆44Updated 2 years ago
Alternatives and similar repositories for LPINNs:
Users that are interested in LPINNs are comparing it to the libraries listed below
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆23Updated 3 years ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆37Updated 8 months ago
- Multifidelity DeepONet☆27Updated last year
- ☆33Updated 2 years ago
- Competitive Physics Informed Networks☆26Updated 3 months ago
- POD-PINN code and manuscript☆47Updated 2 months ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆14Updated 2 years ago
- Basic implementation of physics-informed neural network with pytorch.☆53Updated 2 years ago
- MIONet: Learning multiple-input operators via tensor product☆29Updated 2 years ago
- DeepONet extrapolation☆25Updated last year
- Physics Informed Neural Network (PINN) for Burgers' equation.☆66Updated 4 months ago
- ☆33Updated 3 weeks ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆24Updated last year
- Physics Informed Neural Networks: a starting step for CFD specialists☆27Updated 2 years ago
- Implementation of fast PINN optimization with RBA weights☆45Updated 3 months ago
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆22Updated this week
- Examples implementing physics-informed neural networks (PINN) in Pytorch☆58Updated 3 years ago
- Multi-fidelity reduced-order surrogate modeling☆17Updated last month
- ☆25Updated 2 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆47Updated 4 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆60Updated 2 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆61Updated last year
- Tensoflow 2 implementation of physics informed deep learning.☆26Updated 4 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆19Updated last year
- A Physics-Informed Neural Network to solve 2D steady-state heat equations.☆121Updated 3 weeks ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆49Updated 2 years ago
- Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose☆43Updated 2 years ago
- A Physics-Informed Neural Network for solving Burgers' equation.☆28Updated 9 months ago
- ☆101Updated 2 months ago
- Implementation of PINNs in TensorFlow 2☆73Updated last year