LRydin / KramersMoyalLinks
kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order
☆75Updated 7 months ago
Alternatives and similar repositories for KramersMoyal
Users that are interested in KramersMoyal are comparing it to the libraries listed below
Sorting:
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆26Updated 3 years ago
- Numerical integration of Ito or Stratonovich SDEs☆167Updated 2 years ago
- Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation☆12Updated 3 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆78Updated 2 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆56Updated 3 years ago
- Solve ODEs fast, with support for PyMC☆114Updated last year
- Methods for numerical differentiation of noisy data in python☆120Updated this week
- Probabilistic Inference on Noisy Time Series☆238Updated 6 months ago
- Python codes for Introduction to Computational Stochastic PDE☆44Updated 6 months ago
- Optimal numerical differentiation of noisy time series data in python.☆64Updated last month
- a collection of modern sparse (regularized) linear regression algorithms.☆64Updated 5 years ago
- Numerically solve the Fokker-Planck equation in N dimensions☆91Updated last year
- Python package for computing and visualizing Lagrangian Descriptors in Dynamical Systems☆21Updated 3 years ago
- Python version of Rick Chartrand's algorithm for numerical differentiation of noisy data☆73Updated 4 years ago
- Symbolic Identification of Non-linear Dynamics. The method generalizes the SINDy algorithm by combining sparse and genetic-programming-ba…☆76Updated 2 years ago
- Quasi-Monte Carlo point generators, automatic transformations, and adaptive stopping criteria☆75Updated this week
- Software to train neural networks via Koopman operator theory (see Dogra and Redman "Optimizing Neural Networks via Koopman Operator Theo…☆21Updated 2 years ago
- A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software☆61Updated last year
- Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high perf…☆228Updated 2 years ago
- Stiff Neural Ordinary Differential Equations☆34Updated 2 years ago
- ☆72Updated 4 years ago
- A list of Python-based MCMC & ABC packages☆123Updated 2 months ago
- A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method☆81Updated last month
- Simulation-Enabled Prediction, Inference, and Analysis: physics-informed statistical learning.☆35Updated last year
- Repository for Deterministic Particle Flow Control framework☆10Updated 2 years ago
- Python-based Derivative-Free Optimization with Bound Constraints☆85Updated 10 months ago
- Design of experiments for model discrimination using Gaussian process surrogate models☆36Updated 6 years ago
- Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"☆30Updated last year
- ☆99Updated last year
- Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations☆154Updated 5 years ago