Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach

arXiv:2302.13143v2 Announce Type: replace
Abstract: While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date, PINNs have not been successful in simulating multi-scale and singular perturbation problems. In this work, we present a new training paradigm referred to as "gradient boosting" (GB), which significantly enhances the performance of physics informed neural networks (PINNs). Rather than learning the solution of a given PDE using a single neural network directly, our algorithm employs a sequence of neural networks …

abstract arxiv boosting cs.lg cs.na ensemble gradient math.na networks neural networks paradigm physics physics-informed scale singular training type work