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What is Data-Driven PDE Discovery with PINNs about?

This document discusses using physics-informed neural networks to discover nonlinear partial differential equations from limited data. It introduces two approaches: continuous time models that use scattered spatiotemporal data, and discrete time models that use snapshots. As an example, the method is applied to Burgers

Author

josephpraful

Language

EN