Physics-Informed Neural Networks
PINNs for forward and inverse PDE problems.
Physics-Informed Neural Networks. PINNs for forward and inverse PDE problems.
Recent technical contributions
A handful of recent papers carry the methodological frontier of physics-informed neural networks forward. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Raissi et al., 2019) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for physics-informed neural networks include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- paper · primary · 2019raissi-2019, perdikaris-2019, karniadakis-2019
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