Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics
Texas State University · University of Minho · +2 more institutions
Abstract
Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural…
Citation impact
- FWCI
- 47.61
- Percentile
- 100%
- References
- 427
Authors
7Topics & keywords
- Multiphysics
- Artificial neural network
- Artificial intelligence
- Deep learning
- Computer science
- Nervous system network models
- Machine learning
- Recurrent neural network