Alternatives and similar repositories for Incremental-Gaussian-Process-Regression-IGPR:

Users that are interested in Incremental-Gaussian-Process-Regression-IGPR are comparing it to the libraries listed below

• xl402 / deep-koopman
  IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control
  ☆45Updated 4 years ago
• aidanscannell / mogpe
  Mixtures of Gaussian Process Experts in GPflow/TensorFlow
  ☆11Updated 2 years ago
• Cafolkes / keedmd
  Repository for construction of Koopman eigenfunctions for unknown dynamical systems and identification of a lifted state-space model usin…
  ☆27Updated 2 years ago
• vmartinezalvarez / DyNODE
  DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control
  ☆23Updated 4 years ago
• sorooshafiee / WKF
  ☆14Updated 6 years ago
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆51Updated 3 years ago
• fmeier / local_gaussian_regression
  ☆11Updated 7 years ago
• erichanslee / GP_Derivatives
  Scalable Gaussian Process Regression with Derivatives
  ☆38Updated 6 years ago
• Stanford-ILIAD / active-preference-based-gpr
  Companion code for RSS 2020 paper: "Active Preference-Based Gaussian Process Regression for Reward Learning"
  ☆40Updated last year
• i-abr / mpc-koopman
  Model-based Control using Koopman Operators
  ☆52Updated 4 years ago
• clegaard / deep_learning_for_dynamical_systems
  ☆86Updated 2 years ago
• jgosmann / goppy
  Gaussian Online Processes for Python
  ☆16Updated 2 months ago
• sisl / variational_koopman
  Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…
  ☆38Updated 5 years ago
• williamedwards / autompc
  Automatic Tuning for Data-driven Model Predictive Control
  ☆62Updated last year
• dgedon / DeepSSM_SysID
  Official PyTorch implementation of "Deep State Space Models for Nonlinear System Identification", 2020.
  ☆91Updated 3 years ago
• agijsberts / ssgpr
  Batch and incremental Sparse Spectrum Gaussian Process for Regression
  ☆10Updated 4 years ago
• donken / NeuralOC
  A Neural Network Approach for Real-Time High-Dimensional Optimal Control
  ☆26Updated 2 years ago
• manschuer / SRGP
  stochastic recursive Gaussian Process (SRGP)
  ☆20Updated 4 years ago
• ablancha / gpsearch
  Bayesian optimization and active learning with likelihood-weighted acquisition functions
  ☆16Updated 9 months ago
• asikist / nnc
  A framework for neural network control of dynamical systems over graphs.
  ☆58Updated 2 years ago
• TUM-ITR / koopcore
  Koopman Kernels for Learning Dynamical Systems from Trajectory Data
  ☆24Updated last year
• forgi86 / dynonet
  "dynoNet: A neural network architecture for learning dynamical systems" by Marco Forgione and Dario Piga
  ☆44Updated 10 months ago
• wjmaddox / online_vargp
  Online variational GPs
  ☆32Updated last year
• pnnl / deps_arXiv2020
  Differentiable predictive control (DPC) policy optimization examples.
  ☆53Updated last year
• decargroup / pykoop
  Koopman operator identification library in Python, compatible with `scikit-learn`
  ☆70Updated 4 months ago
• haozhg / oml
  AI4Science: Efficient data-driven Online Model Learning (OML) / system identification and control
  ☆30Updated 2 years ago
• thangbui / streaming_sparse_gp
  Streaming sparse Gaussian process approximations
  ☆64Updated 2 years ago
• YunzhuLi / CompositionalKoopmanOperators
  [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control
  ☆88Updated 3 years ago
• lubaroli / dust
  This is the code repository for the DuSt-MPC paper, published at Robotics: Science and Systems (RSS) 2021.
  ☆24Updated 3 years ago
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆47Updated 3 years ago