Physics Informed Neural Networks and Neural Differential Equations

Learn two complementary scientific machine learning approaches through this tutorial that explores Neural Ordinary/Partial Differential Equations (ODEs/PDEs) and Physics-Informed Neural Networks (PINNs). Discover how Neural ODEs approximate unknown differential equations from available data points in solution spaces, while PINNs solve known differential equations that are numerically challenging. Compare both methodologies using the same differential equation to understand their distinct use cases and applications. Access practical implementation through provided code examples to reinforce theoretical concepts and gain hands-on experience with these advanced scientific computing techniques.