Summary of Pinnde: Physics-informed Neural Networks For Solving Differential Equations, by Jason Matthews et al.
PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
by Jason Matthews, Alex Bihlo
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel open-source Python library called PinnDE is proposed for solving differential equations using physics-informed neural networks (PINNs) and deep operator networks (DeepONets). This library leverages the strengths of both approaches, offering a unified framework for approximating solutions. The summary reviews PINNs and DeepONets, introducing PinnDE’s structure and usage. Worked examples demonstrate the effectiveness of PinnDE in solving differential equations with both PINN-based and DeepONet-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PinnDE is a new tool that helps computers solve difficult math problems called differential equations. It uses two special kinds of artificial intelligence (AI) models: physics-informed neural networks (PINNs) and deep operator networks (DeepONets). These models are great at finding solutions to these math problems. The PinnDE library makes it easy to use both PINNs and DeepONets to solve differential equations, which is useful for many fields like physics, engineering, and computer science. |
