DENG-MIT / KAN-ODEs
The code is associated with the paper entitled "KAN-ODEs: Kolmogorov-Arnold Network Ordinary Differential Equations for Learning Dynamical Systems and Hidden Physics"
☆19Updated 2 months ago
Alternatives and similar repositories for KAN-ODEs:
Users that are interested in KAN-ODEs are comparing it to the libraries listed below
- Material for workshop and autumn school on scientific machine learning 2023☆19Updated last year
- PDE Preserved Neural Network☆42Updated 6 months ago
- Pseudospectral Kolmogorov Flow Solver☆37Updated last year
- Code for "Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains"☆15Updated 8 months ago
- Update PDEKoopman code to Tensorflow 2☆22Updated 3 years ago
- A library for dimensionality reduction on spatial-temporal PDE☆62Updated 9 months ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆29Updated 6 months ago
- ☆10Updated 4 months ago
- ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equat…☆97Updated last week
- Physics-informed neural networks☆14Updated 4 years ago
- ☆46Updated this week
- [Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (≥46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled T…☆54Updated 2 months ago
- Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution …☆23Updated 3 years ago
- ☆24Updated last year
- Codes associated with the manuscript titled "Multi-stage neural networks: Function approximator of machine precision"☆36Updated 9 months ago
- ☆41Updated last year
- PinnDE: A Python library for solving differential equations with physics informed neural networks and deep operator networks☆16Updated 2 months ago
- MIONet: Learning multiple-input operators via tensor product☆29Updated 2 years ago
- Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.☆69Updated 2 months ago
- Sparse Physics-based and Interpretable Neural Networks☆46Updated 3 years ago
- Transfer learning on PINNs for tracking hemodynamics☆10Updated 5 months ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆37Updated 8 months ago
- ☆11Updated last month
- This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Desce…☆17Updated 2 months ago
- Deep finite volume method☆18Updated 6 months ago
- Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Network…☆11Updated 9 months ago
- ☆96Updated last week
- Practicum on Supervised Learning in Function Spaces☆32Updated 2 years ago