Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data

4 March 2021

Papers citing "Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data"

6 / 6 papers shown

Title
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification Thomas Beckers Tom Z. Jiahao George J. Pappas 31 2 0 15 May 2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior Thomas Beckers Jacob H. Seidman P. Perdikaris George J. Pappas PINN 29 17 0 15 May 2023
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks Mohamed Aziz Bhouri M. Joly Robert Yu S. Sarkar P. Perdikaris BDL UQCV AI4CE 19 1 0 14 Feb 2023
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed Shian Du Yihong Luo Wei Chen Jian Xu Delu Zeng 29 7 0 19 Mar 2022
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling Kookjin Lee Nathaniel Trask P. Stinis 35 25 0 11 Sep 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 24 16 0 21 Jun 2021