raj-brown / APMA_2070_ENGN_2912_SPRING_2024Links
☆64Updated 9 months ago
Alternatives and similar repositories for APMA_2070_ENGN_2912_SPRING_2024
Users that are interested in APMA_2070_ENGN_2912_SPRING_2024 are comparing it to the libraries listed below
Sorting:
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- ☆54Updated 2 years ago
- This repository is the official project page of the course AI in the Sciences and Engineering, ETH Zurich.☆247Updated 2 months ago
- ☆145Updated 3 years ago
- ETH Zürich Deep Learning in Scientific Computing Master's course 2023☆117Updated last year
- ☆50Updated 2 years ago
- ☆170Updated last year
- Applications of PINOs☆129Updated 2 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆203Updated 2 years ago
- ☆218Updated 3 years ago
- PINN (Physics-Informed Neural Networks) on Navier-Stokes Equations☆107Updated 2 years ago
- Tutorials on deep learning, Python, and dissipative particle dynamics☆194Updated 3 years ago
- ☆144Updated 9 months ago
- ☆97Updated 3 years ago
- ☆50Updated 7 months ago
- Basic implementation of physics-informed neural network with pytorch.☆73Updated 2 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆137Updated 3 years ago
- Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.☆150Updated 5 years ago
- Original implementation of fast PINN optimization with RBA weights☆57Updated 3 months ago
- Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose☆49Updated 2 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆72Updated 2 years ago
- hPINN: Physics-informed neural networks with hard constraints☆140Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- ☆111Updated 6 months ago
- Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube proble…☆181Updated 2 years ago
- MIONet: Learning multiple-input operators via tensor product☆37Updated 2 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆74Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆80Updated 3 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆95Updated 3 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago