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This document reviews the application of neural networks in scientific computing, focusing on three frameworks: physics-guided neural networks (PgNNs), physics-informed neural networks (PiNNs), and physics-encoded neural networks (PeNNs). It discusses their architectures, advantages, limitations, and potential applications in fields like fluid and solid mechanics, highlighting the importance of integrating physics into machine learning models to enhance predictive capabilities. The review also addresses the

Author

jaikrishnank2006

Language

EN