PyTorch Implementation for Structure-preserving GPs. This is part of the publication "Structure-preserving Gaussian Process dynamics" (ECML 2022).

Purpose of the project

This software is a research prototype, solely developed for and published as part of the publication "Structure-preserving Gaussian Process dynamics" (ECML 2022). It will neither be maintained nor monitored in any way.

Installation

Packages are listed in requirements.txt

Experiments

To perform experiments, the following scripts are available:

• pendulum: pend_SGPD.py produces results for symplectic Euler-based SGPD
• nonseparable Hamiltonian nonseparable_SGPD.py: produces results for midpoint-based SGPD method

Components

Integrators

• explicit_integrator.py: includes standard implicit integrators as Euler and Heun-method.
• nonsep_midpoint.py: includes the Hamiltonian implicit midpoint method as used for the nonseparable Hamiltonian system.
• RB_midpoint.py: standard implicit midpoint method.
• symplectic_integrator.py: includes explicit symplectic integrators as the explicit Euler method.
• The custom backpropagation for the implicit integrators is contained in integrator/Implicit.

Loss

• The log-probability is contained in Noise/noise_nd.py.

Models

• sparse_GP.matherons_rule contains a
  • sparse GP implementation: MatheronGP
  • an implementation for the derivatives of a GP: MatheronDerivative
  • a GP with quadratic contrains on the dynamics: ODE
  • Function to stack independent GPs: StackedGP

LICENSE

structure-preserving Gaussian processes is open-sourced under the AGPL-3.0 license. See the LICENSE file for details.