Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network

Understand

Big-data-based artificial intelligence (AI) supports profound evolution in almost all of science and technology.

• However, modeling and forecasting multi-physical systems remain a challenge due to unavoidable data scarcity and noise.
• Improving the generalization ability of neural networks by "teaching" domain knowledge and developing a new generation of models combined with the physical laws have become promising areas of machine learning research.
• Different from "deep" fully-connected neural networks embedded with physical information (PINN), a novel shallow framework named physics-informed convolutional network (PICN) is recommended from a CNN perspective, in which the physical field is generated by a deconvolution layer and a single convolution layer.