rsmath / dt-pinnLinks
Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations
☆28Updated 2 years ago
Alternatives and similar repositories for dt-pinn
Users that are interested in dt-pinn are comparing it to the libraries listed below
Sorting:
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆32Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆51Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆30Updated 3 years ago
- MIONet: Learning multiple-input operators via tensor product☆37Updated 2 years ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 7 months ago
- POD-PINN code and manuscript☆53Updated 10 months ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 3 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Physics-informed radial basis network☆32Updated last year
- ☆29Updated 2 years ago
- Code accompanying "Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks", Maddu et al., 2021☆12Updated 3 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆25Updated last year
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- Code for "Robust flow field reconstruction from limited measurements vis sparse representation" (J. Callaham, K. Maeda, and S. Brunton 20…☆14Updated 6 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated 2 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆25Updated 2 years ago
- Physics Informed Neural Network (PINN) for the 2D Navier-Stokes equation☆29Updated 3 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆16Updated last year
- ☆61Updated 5 months ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆41Updated 2 years ago
- Implementation of 'Physics-Informed Neural Networks for Shell Structures' (European Journal of Mechanics A)☆43Updated last year
- Physics-guided neural network framework for elastic plates☆46Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- ☆54Updated 2 years ago
- ☆32Updated 3 years ago
- ☆14Updated 3 years ago
- ☆29Updated 8 months ago