dynamicslab / langevin-regressionLinks
Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton
☆25Updated 3 years ago
Alternatives and similar repositories for langevin-regression
Users that are interested in langevin-regression are comparing it to the libraries listed below
Sorting:
- combination of sparse identification of nonlinear dynamics with Akaike information criteria☆16Updated 7 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69Updated 5 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆29Updated 2 years ago
- PySpectral is a Python package for solving the partial differential equation (PDE) of Burgers' equation in its deterministic and stochast…☆14Updated 2 years ago
- Two Dimensional Fokker-Planck Solver using Matlab☆19Updated 5 years ago
- kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order☆73Updated 5 months ago
- ☆29Updated 6 years ago
- Simulation-Enabled Prediction, Inference, and Analysis: physics-informed statistical learning.☆35Updated last year
- This codes calculates the dimensionalized POD and uses SINDy from the PySINDy python package to build a data-driven model for it. The cod…☆19Updated 4 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆55Updated 3 years ago
- ☆14Updated 3 years ago
- ☆16Updated 9 months ago
- Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation☆12Updated 3 years ago
- Update PDEKoopman code to Tensorflow 2☆23Updated 4 years ago
- ☆18Updated 4 years ago
- The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural …☆15Updated 2 years ago
- Differentiable interface to FEniCS for JAX☆54Updated 4 years ago
- Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning☆16Updated last year
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆56Updated 4 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆74Updated 2 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆19Updated 2 years ago
- Stiff Neural Ordinary Differential Equations☆34Updated last year
- Convolutional Solvers for Partial Differential Equations☆28Updated 4 years ago
- Pseudospectral Kolmogorov Flow Solver☆40Updated last year
- PyTorch implementation of GMLS-Nets. Machine learning methods for scattered unstructured data sets. Methods for learning differential op…☆26Updated last year
- ☆29Updated 2 years ago
- Machine learning algorithms for discovering dimensionless groups from simulation and experimental data☆12Updated 2 years ago
- ☆41Updated 7 years ago
- ☆10Updated 2 years ago