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Learning Hybrid Dynamics Models With Simulator-Informed Latent States
In this paper, we propose a new approach to hybrid modeling, where we inform the latent states of a learned model via a black-box simulator.
Exact Inference for Continuous-Time Gaussian Process Dynamics
This is crucial to sample consistent predictions from the dynamics model.
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
This filtering technique combines two signals by applying a high-pass filter to one signal, and low-pass filtering the other.
Structure-preserving Gaussian Process Dynamics
On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time.
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