dcshapiro / AI-FeynmanLinks
☆30Updated 5 years ago
Alternatives and similar repositories for AI-Feynman
Users that are interested in AI-Feynman are comparing it to the libraries listed below
Sorting:
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆79Updated 2 years ago
- ☆116Updated 6 years ago
- ☆73Updated 5 years ago
- A computational framework for finding symbolic expressions from physical datasets.☆61Updated 2 years ago
- ☆195Updated 7 months ago
- PyTorch implementation of GMLS-Nets. Machine learning methods for scattered unstructured data sets. Methods for learning differential op…☆27Updated last month
- A library for dimensionality reduction on spatial-temporal PDE☆70Updated last year
- Equation Learner, a neural network approach to symbolic regression☆83Updated last year
- ☆49Updated last year
- Datasets and code for results presented in the BOON paper☆45Updated 2 years ago
- ☆41Updated 7 years ago
- ☆21Updated 5 years ago
- ☆21Updated 2 years ago
- PySensors is a Python package for sparse sensor placement☆105Updated last week
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆158Updated 9 months ago
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆116Updated 3 years ago
- ☆21Updated 2 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- Learning Green's functions of partial differential equations with deep learning.☆72Updated last year
- Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"☆28Updated 5 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆26Updated 3 years ago
- Transformers for modeling physical systems☆145Updated 2 years ago
- Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"☆77Updated 2 years ago
- physics-guided neural networks (phygnn)☆99Updated last month
- ☆35Updated 2 years ago
- ☆264Updated 2 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- Datasets and code for results presented in the ProbConserv paper☆56Updated last year
- Bootcamp notebooks☆60Updated 2 months ago
- ☆175Updated 2 years ago