Physics-Informed Neural Networks and Extensions
arXiv:2408.16806v119 citationsh-index: 142
Originality Synthesis-oriented
AI Analysis
It addresses the problem of integrating physics into machine learning for scientific applications, but is incremental as it reviews and extends existing work.
The paper reviews Physics-Informed Neural Networks (PINNs), a method for scientific machine learning, and presents extensions with an example in data-driven discovery of governing differential equations.
In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of governing differential equations.