cagrell / gp_constrLinks
Python model for constrained GP
☆24Updated 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:
- Exploring how to to deal with uncertain inputs with gaussian process regression models.☆27Updated 4 years ago
- Code for efficiently sampling functions from GP(flow) posteriors☆72Updated 4 years ago
- ☆29Updated 2 years ago
- Deep GPs built on top of TensorFlow/Keras and GPflow☆125Updated 8 months ago
- A Python package to learn the Koopman operator.☆57Updated 7 months ago
- Nonparametric Differential Equation Modeling☆53Updated last year
- Flexible and efficient tools for high-dimensional approximation, scientific machine learning and uncertainty quantification.☆62Updated last week
- Stochastic variational heteroscedastic Gaussian process☆15Updated 6 years ago
- Heterogeneous Multi-output Gaussian Processes☆52Updated 5 years ago
- Offline Contextual Bayesian Optimization☆14Updated last year
- Symbolic Identification of Non-linear Dynamics. The method generalizes the SINDy algorithm by combining sparse and genetic-programming-ba…☆76Updated 2 years ago
- A general-purpose Python package for Koopman theory using deep learning.☆103Updated 4 months ago
- Multi-Output Gaussian Process Toolkit☆175Updated 3 weeks ago
- This repository contains the code for the paper "Geometry-aware Bayesian Optimization in Roboticsusing Riemannian Matérn Kernels" (CoRL'2…☆19Updated last year
- GPz 2.0: Heteroscedastic Gaussian processes for uncertain and incomplete data☆48Updated 4 years ago
- A Tensorflow based library for Time Series Modelling with Gaussian Processes☆32Updated 11 months ago
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆56Updated 3 years ago
- ☆109Updated 4 years ago
- Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"☆62Updated 4 years ago
- ☆181Updated 2 months ago
- Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"☆30Updated last year
- Learning unknown ODE models with Gaussian processes☆26Updated 6 years ago
- ☆11Updated 2 years ago
- A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch☆111Updated 3 months ago
- Data-driven dynamical systems toolbox.☆74Updated last month
- Literature and light wrappers for gaussian process models.☆47Updated 4 years ago
- Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling☆48Updated 3 years ago
- Implementation of Lagrangian Neural Networks in PyTorch☆12Updated 4 years ago
- SAASBO: a package for high-dimensional bayesian optimization☆43Updated 3 years ago
- Python package 'dgpsi' for deep and linked Gaussian process emulations☆27Updated 5 months ago