cagrell / gp_constrLinks
Python model for constrained GP
☆26Updated 4 years ago
Alternatives and similar repositories for gp_constr
Users that are interested in gp_constr are comparing it to the libraries listed below
Sorting:
- A Python package to learn the Koopman operator.☆63Updated last week
- Exploring how to to deal with uncertain inputs with gaussian process regression models.☆27Updated 4 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆127Updated last year
- Code for efficiently sampling functions from GP(flow) posteriors☆73Updated 5 years ago
- ☆28Updated 3 years ago
- Multi-Output Gaussian Process Toolkit☆179Updated 5 months ago
- This repository contains the code for the paper "Geometry-aware Bayesian Optimization in Roboticsusing Riemannian Matérn Kernels" (CoRL'2…☆21Updated 2 years ago
- A general-purpose Python package for Koopman theory using deep learning.☆115Updated last month
- GPz 2.0: Heteroscedastic Gaussian processes for uncertain and incomplete data☆49Updated 4 years ago
- Stochastic variational heteroscedastic Gaussian process☆15Updated 6 years ago
- Heterogeneous Multi-output Gaussian Processes☆54Updated 5 years ago
- Offline Contextual Bayesian Optimization☆14Updated 2 years ago
- neural networks to learn Koopman eigenfunctions☆446Updated last year
- Learning Neural Differential Algebraic Equations via Operator Splitting☆21Updated 3 months ago
- ☆31Updated 8 months ago
- A package for computing data-driven approximations to the Koopman operator.☆389Updated last year
- Streaming sparse Gaussian process approximations☆68Updated 3 years ago
- Data-driven dynamical systems toolbox.☆77Updated 3 weeks ago
- Equation Learner, a neural network approach to symbolic regression☆84Updated last year
- A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch☆116Updated 2 weeks ago
- Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"☆64Updated 4 years ago
- Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"☆30Updated last year
- Flexible and efficient tools for high-dimensional approximation, scientific machine learning and uncertainty quantification.☆65Updated this week
- ☆374Updated 4 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
- SAASBO: a package for high-dimensional bayesian optimization☆49Updated 4 years ago
- ☆89Updated 2 years ago
- Bayesian Neural Network Surrogates for Bayesian Optimization☆63Updated last year
- Light-weighted code for Orthogonal Additive Gaussian Processes☆43Updated last year
- ☆11Updated last year