Monotonic Gaussian process for physics-constrained machine learning with materials science applications. (arXiv:2209.00628v1 [cs.LG])

Physics-constrained machine learning is emerging as an important topic in the
field of machine learning for physics. One of the most significant advantages
of incorporating physics constraints into machine learning methods is that the
resulting model requires significantly less data to train. By incorporating
physical rules into the machine learning formulation itself, the predictions
are expected to be physically plausible. Gaussian process (GP) is perhaps one
of the most common methods in machine learning for small datasets. In this
paper, …

applications arxiv learning machine machine learning materials materials science physics process science