KindXiaoming / aipoincare
Counting the number of conservation laws from trajectory data
☆21Updated 4 years ago
Alternatives and similar repositories for aipoincare:
Users that are interested in aipoincare are comparing it to the libraries listed below
- [DMLR] Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery☆33Updated last year
- Code for the paper "Rational neural networks", NeurIPS 2020☆28Updated 4 years ago
- Schrodinger Principal Component Analysis☆22Updated 4 years ago
- ☆21Updated 5 months ago
- The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural …☆14Updated 2 years ago
- Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"☆49Updated last year
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆25Updated 3 years ago
- ☆27Updated 3 years ago
- Refining continuous-in-depth neural networks☆39Updated 3 years ago
- AI Hilbert is an algebraic geometric based discovery system (based on Putinar's Positivstellensatz), that enables the discovery of fundam…☆30Updated 8 months ago
- code for "Neural Conservation Laws A Divergence-Free Perspective".☆37Updated 2 years ago
- Pytorch source code for arXiv paper Neural Network Renormalization Group, a generative model using variational renormalization group and …☆79Updated 5 years ago
- ☆31Updated 9 months ago
- ☆31Updated 4 years ago
- A Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.☆28Updated last month
- Deterministic particle dynamics for simulating Fokker-Planck probability flows☆24Updated 2 years ago
- Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics☆51Updated 2 years ago
- ☆11Updated 4 years ago
- ☆22Updated 9 months ago
- Code for Lie Symmetries SSL paper☆20Updated last year
- Accompanying code for "Weak form generalized Hamiltonian learning"☆9Updated 4 years ago
- Learning Green's functions of partial differential equations with deep learning.☆65Updated last year
- Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling☆36Updated 3 years ago
- Symplectic Recurrent Neural Networks☆28Updated 2 years ago
- predicting equations from raw data with deep learning☆56Updated 6 years ago
- A software package for flexible HPC GPs☆16Updated last month
- code associated with paper "Sparse Bayesian Optimization"☆26Updated last year
- Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks☆55Updated last week
- Code to go along the lecture course "Advanced Machine Learning for Physics, Science, and Artificial Scientific Discovery"☆52Updated 7 months ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago