Physics Informed Deep Kernel Learning. (arXiv:2006.04976v2 [stat.ML] UPDATED)

Deep kernel learning is a promising combination of deep neural networks and
nonparametric function learning. However, as a data driven approach, the
performance of deep kernel learning can still be restricted by scarce or
insufficient data, especially in extrapolation tasks. To address these
limitations, we propose Physics Informed Deep Kernel Learning (PI-DKL) that
exploits physics knowledge represented by differential equations with latent
sources. Specifically, we use the posterior function sample of the Gaussian
process as the surrogate for the solution …

arxiv kernel learning ml physics