Learning ODE Models with Qualitative Structure Using Gaussian Processes

Abstract

Recent advances in learning techniques have enabled the modelling of dynamical systems for scientific and engineering applications directly from data. However, in many contexts explicit data collection is expensive and learning algorithms must be data-efficient to be feasible. This suggests using additional qualitative information about the system, which is often available from prior experiments or domain knowledge. We propose an approach to learning a vector field of differential equations using sparse Gaussian Processes that allows us to combine data and additional structural information, like Lie Group symmetries and fixed points. We show that this combination improves extrapolation performance and long-term behaviour significantly, while also reducing the computational cost.

References in corpus (2)

• Actively Learning Gaussian Process Dynamics
• On Simulation and Trajectory Prediction with Gaussian Process Dynamics

Cited by in corpus (4)

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• Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
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• MAGI-X: Manifold-Constrained Gaussian Process Inference for Unknown System Dynamics