benmoseley / AISE-2024
ETH Zürich AI in the Sciences and Engineering Master's course 2024
☆28Updated 6 months ago
Alternatives and similar repositories for AISE-2024:
Users that are interested in AISE-2024 are comparing it to the libraries listed below
- ☆52Updated 2 years ago
- This repository is the official project page of the course AI in the Sciences and Engineering, ETH Zurich.☆157Updated 2 months ago
- ☆88Updated 3 years ago
- Applications of PINOs☆112Updated 2 years ago
- DeepONet extrapolation☆25Updated last year
- ☆25Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆46Updated 3 years ago
- PDE Preserved Neural Network☆43Updated 6 months ago
- ☆12Updated last year
- ☆51Updated 2 months ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆29Updated 7 months ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆67Updated last year
- ☆22Updated 6 months ago
- Code for "Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains"☆15Updated 8 months ago
- Example problems in Physics informed neural network in JAX☆77Updated last year
- MIONet: Learning multiple-input operators via tensor product☆29Updated 2 years ago
- Spectral Neural Operator☆75Updated last year
- ETH Zürich Deep Learning in Scientific Computing Master's course 2023☆106Updated 6 months ago
- This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs☆146Updated 3 months ago
- Codes associated with the manuscript titled "Multi-stage neural networks: Function approximator of machine precision"☆36Updated 9 months ago
- ☆29Updated last year
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆61Updated last year
- Multifidelity DeepONet☆27Updated last year
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆24Updated last year
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆62Updated 2 years ago
- ☆11Updated 2 years ago
- ☆155Updated 11 months ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆24Updated 3 years ago
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆23Updated 9 months ago
- ☆103Updated 6 months ago