Adjoint-aided inference of Gaussian process driven differential equations.
Paterne GahunguChristopher W. LanyonMauricio A. ÁlvarezEngineer BainomugishaMichael Thomas SmithRichard D. WilkinsonPublished in: CoRR (2022)
Keyphrases
- differential equations
- gaussian process
- covariance function
- expectation propagation
- gaussian processes
- gaussian process regression
- approximate inference
- regression model
- dynamical systems
- model selection
- hyperparameters
- latent variables
- bayesian framework
- semi supervised
- bayesian networks
- variational bayes
- bayesian inference
- dynamic bayesian networks
- probabilistic inference
- optimal control
- partial differential equations
- gaussian process models
- machine learning
- multiscale
- graphical models
- probabilistic model