Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes

no code implementations • 8 Apr 2024 • Ming Zhong, Dehao Liu, Raymundo Arroyave, Ulisses Braga-Neto

This paper proposes a semi-supervised methodology for training physics-informed machine learning methods.

Gaussian Processes Physics-informed machine learning

A Dual-Dimer Method for Training Physics-Constrained Neural Networks with Minimax Architecture

no code implementations • 1 May 2020 • Dehao Liu, Yan Wang

Data sparsity is a common issue to train machine learning tools such as neural networks for engineering and scientific applications, where experiments and simulations are expensive.