Prediction with Approximated Gaussian Process Dynamical Models

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Prediction with Approximated Gaussian Process Dynamical Models

25 June 2020

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Papers citing "Prediction with Approximated Gaussian Process Dynamical Models"

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Title
Koopman-Equivariant Gaussian Processes Petar Bevanda Max Beier Armin Lederer A. Capone Stefan Sosnowski Sandra Hirche AI4TS 102 2 0 10 Feb 2025
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework Manish Prajapat Amon Lahr Johannes Köhler Andreas Krause Melanie Zeilinger 54 3 0 13 Sep 2024
Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models Kaiyuan Tan Peilun Li Thomas Beckers AI4CE 54 3 0 17 Jun 2024
Gaussian Process-Based Learning Control of Underactuated Balance Robots with an External and Internal Convertible Modeling Structure Feng Han Jingang Yi 59 1 0 15 Dec 2023
Gaussian Process-Enhanced, External and Internal Convertible (EIC) Form-Based Control of Underactuated Balance Robots Feng Han Jingang Yi 47 2 0 27 Sep 2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior Thomas Beckers Jacob H. Seidman P. Perdikaris George J. Pappas PINN 82 17 0 15 May 2023
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States Robert Lefringhausen Supitsana Srithasan Armin Lederer Sandra Hirche 71 6 0 31 Mar 2023
Sequential Estimation of Gaussian Process-based Deep State-Space Models Yuhao Liu Marzieh Ajirak Petar M. Djurić 96 14 0 29 Jan 2023