ORF522 / companionLinks
Supporting material for Princeton ORF522
☆14Updated 2 months ago
Alternatives and similar repositories for companion
Users that are interested in companion are comparing it to the libraries listed below
Sorting:
- a collection of modern sparse (regularized) linear regression algorithms.☆65Updated 5 years ago
- ☆28Updated 3 years ago
- Data-driven dynamical systems toolbox.☆77Updated 3 weeks ago
- APPM 5630 at CU Boulder☆51Updated 3 months ago
- Harvard Applied Math 205: Code Examples☆93Updated 3 years ago
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆32Updated last year
- ☆87Updated 2 years ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆59Updated 3 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆26Updated 3 years ago
- Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems☆149Updated 2 weeks ago
- ☆30Updated 3 years ago
- ☆21Updated 7 years ago
- SINDy (Sparse Identification of Nonlinear Dynamics) algorithms☆79Updated 2 years ago
- How to train a neural ODE for time series/weather forecasting☆39Updated 2 years ago
- Symbolic Identification of Non-linear Dynamics. The method generalizes the SINDy algorithm by combining sparse and genetic-programming-ba…☆81Updated 3 years ago
- kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order☆75Updated 10 months ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 7 years ago
- Learning unknown ODE models with Gaussian processes☆27Updated 7 years ago
- Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation☆12Updated 4 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
- ☆30Updated 7 years ago
- Nonparametric Differential Equation Modeling☆56Updated last year
- Neat Bayesian machine learning examples☆58Updated last week
- Linear and non-linear spectral forecasting algorithms☆138Updated 4 years ago
- Long-term probabilistic forecasting of quasiperiodic phenomena using Koopman theory☆35Updated 3 years ago
- Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations☆156Updated 5 years ago
- Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high perf…☆233Updated 2 years ago
- Unifying sparse approximations for Gaussian process regression and classification, using Power EP☆22Updated 9 years ago
- Course 5SSD0 - Bayesian Machine Learning and Information Processing☆50Updated 2 months ago
- 18.S096 - Applications of Scientific Machine Learning☆311Updated 3 years ago