martius-lab / EQL_Tensorflow
Tensorflow implementation of equation learner
☆31Updated 4 years ago
Alternatives and similar repositories for EQL_Tensorflow:
Users that are interested in EQL_Tensorflow are comparing it to the libraries listed below
- Equation Learner, a neural network approach to symbolic regression☆77Updated 6 months ago
- Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"☆70Updated 2 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- PyTorch implementation of the EQL network, a neural network for symbolic regression☆39Updated 4 years ago
- Source code and Dataset creation for the paper "Neural Symbolic Regression That Scales"☆87Updated 3 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆74Updated 2 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆25Updated 3 years ago
- ☆15Updated 2 years ago
- ☆17Updated last year
- Deep GPs built on top of TensorFlow/Keras and GPflow☆124Updated 6 months ago
- RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical sy…☆94Updated 2 years ago
- Code and files related to random side projects☆21Updated 3 years ago
- Nonparametric Differential Equation Modeling☆53Updated last year
- ☆21Updated 4 years ago
- Numerical integration of Ito or Stratonovich SDEs☆167Updated 2 years ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆34Updated 3 years ago
- ☆72Updated 4 years ago
- kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order☆72Updated 3 months ago
- ☆28Updated 6 years ago
- Hamiltonian Neural Networks for solving Differential Equations☆22Updated 3 years ago
- ☆19Updated 2 years ago
- predicting equations from raw data with deep learning☆57Updated 6 years ago
- SAASBO: a package for high-dimensional bayesian optimization☆41Updated 3 years ago
- ☆41Updated 7 years ago
- Stochastic Physics-Informed Neural Ordinary Differential Equations☆16Updated 2 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆67Updated 4 years ago
- a collection of modern sparse (regularized) linear regression algorithms.☆61Updated 4 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆54Updated 2 years ago
- Heterogeneous Multi-output Gaussian Processes☆52Updated 4 years ago