ORF522 / companionLinks
Supporting material for Princeton ORF522
☆13Updated 6 months ago
Alternatives and similar repositories for companion
Users that are interested in companion are comparing it to the libraries listed below
Sorting:
- ☆29Updated 6 years ago
- Learning unknown ODE models with Gaussian processes☆26Updated 6 years ago
- ☆29Updated 2 years ago
- ☆30Updated 2 years ago
- ☆38Updated 3 years ago
- Nonparametric Differential Equation Modeling☆53Updated last year
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆25Updated 3 years ago
- Data-driven dynamical systems toolbox.☆74Updated 3 weeks ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆55Updated 3 years ago
- Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation☆12Updated 3 years ago
- Library for Bayesian Quadrature☆32Updated 6 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 7 years ago
- Software to train neural networks via Koopman operator theory (see Dogra and Redman "Optimizing Neural Networks via Koopman Operator Theo…☆21Updated 2 years ago
- How to train a neural ODE for time series/weather forecasting☆37Updated 2 years ago
- Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems☆114Updated 2 months ago
- kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order☆73Updated 5 months ago
- Port-Hamiltonian Approach to Neural Network Training☆24Updated 5 years ago
- Python and MATLAB code for Stein Variational sampling methods☆25Updated 6 years ago
- A PyTorch library for all things nonlinear control and reinforcement learning.☆46Updated 3 years ago
- Methods and experiments for assumed density SDE approximations☆12Updated 3 years ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆22Updated last year
- Unifying sparse approximations for Gaussian process regression and classification, using Power EP☆22Updated 8 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- APPM 5630 at CU Boulder☆49Updated 3 weeks ago
- ☆14Updated last year
- Turning SymPy expressions into JAX functions☆45Updated 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
- Set of Lecture at Duke in 2018 by Lenka Zdeborova and Florent Krzakala "Statistical Physics For Optimization and Learning"☆16Updated 5 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 4 years ago
- Dynamic mode decomposition with dependent structure among observables (Graph DMD)☆13Updated 5 years ago