pvlachas / RNN-Lyapunov-SpectrumLinks
A data-driven method to calculate the Lyapunov exponent of a dynamical system employing a GRU-RNN.
☆45Updated last year
Alternatives and similar repositories for RNN-Lyapunov-Spectrum
Users that are interested in RNN-Lyapunov-Spectrum are comparing it to the libraries listed below
Sorting:
- RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical sy…☆95Updated 2 years ago
- A simulation of the Kuramoto-Sivashinsky Equation in Python and MATLAB☆26Updated 6 years ago
- a PyTorch based Reservoir Computing package with Automatic Hyper-Parameter Tuning☆45Updated 2 years ago
- This repository contains code for parallelized prediction of spatiotemporal chaotic data using reservoir computing as described in the pa…☆37Updated 5 years ago
- Deep learning assisted dynamic mode decomposition☆20Updated 3 years ago
- Consistent Koopman Autoencoders☆74Updated 2 years ago
- Discovers high dimensional models from 1D data using deep delay autoencoders☆36Updated 2 years ago
- ☆11Updated 4 years ago
- Spatio-temporal forecasting of Lorenz96 with RC-ESN, RNN-LSTM and ANN☆43Updated 4 years ago
- Code and files related to random side projects☆21Updated 3 years ago
- ☆41Updated 7 years ago
- Linear and non-linear spectral forecasting algorithms☆137Updated 4 years ago
- Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid …☆36Updated 2 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆79Updated 2 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Updated 3 years ago
- Code for paper Sparse identification of nonlinear dynamics with Shallow Recurrent Decoder Networks.☆30Updated 3 weeks ago
- SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations☆21Updated 3 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- ☆19Updated 2 years ago
- ☆48Updated last year
- ☆88Updated 2 years ago
- Codes for Linear and Nonlinear Disambiguation Optimization (LANDO)☆29Updated 3 years ago
- Stochastic Physics-Informed Neural Ordinary Differential Equations☆17Updated 3 years ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆34Updated 3 years ago
- Code for the paper "Next Generation Reservoir Computing"☆180Updated 4 years ago
- ☆21Updated 2 years ago
- Differentiable Physics-informed Graph Networks☆67Updated 5 years ago
- Accompanying code for "State Estimation of a Physical System without Governing Equations"☆89Updated last year
- ☆14Updated 3 years ago