Physics-Informed Variational State-Space Gaussian Processes
Authors: Oliver Hamelijnck, Arno Solin, Theodoros Damoulas
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our methods in a range of synthetic and real-world settings and outperform the current state-of-the-art in both predictive and computational performance. |
| Researcher Affiliation | Academia | Oliver Hamelijnck University of Warwick oliver.hamelijnck@warwick.ac.uk Arno Solin Aalto University arno.solin@aalto.fi Theodoros Damoulas University of Warwick t.damoulas@warwick.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to reproduce experiments is available at https://github.com/ohamelijnck/physs_gp. |
| Open Datasets | Yes | We follow [4] and use the dataset provided by D Asaro et al. [15] that has information from over 1, 000 buoys. |
| Dataset Splits | No | The paper mentions training and testing splits, but does not explicitly state a validation split for model selection or hyperparameter tuning. For example, for the Damped Pendulum: 'generate 20 points in t [0, 6] for training and 200 in t [6, 30] for testing'. |
| Hardware Specification | Yes | All models were run using an Nvidia Titan RTX GPU and an Intel Core i5 CPU. |
| Software Dependencies | No | The paper mentions software components like 'Adam' optimizer, but does not specify version numbers for any libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | All were optimised for 1000 epochs using Adam [32] with a learning rate of 0.01. Both the GP and AUTOIP had an RBF kernel (following Long et al. [40]) and PHYSS-GP used a Matérn-7/2; all with a lengthscale of 1.0. The observation noise was initialised to 0.01 and the collocation 0.001. Both were fixed for the first 40% of training and then released. |