Summary of A Two-stage Imaging Framework Combining Cnn and Physics-informed Neural Networks For Full-inverse Tomography: a Case Study in Electrical Impedance Tomography (eit), by Xuanxuan Yang (1 and 2) et al.
A Two-Stage Imaging Framework Combining CNN and Physics-Informed Neural Networks for Full-Inverse Tomography: A Case Study in Electrical Impedance Tomography (EIT)
by Xuanxuan Yang, Yangming Zhang, Haofeng Chen, Gang Ma, Xiaojie Wang
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Physics (physics.comp-ph)
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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 The proposed two-stage hybrid learning framework combines Convolutional Neural Networks (CNNs) and Physics-Informed Neural Networks (PINNs) to solve Electrical Impedance Tomography (EIT), a highly ill-posed inverse problem. The framework integrates data-driven and model-driven paradigms, blending supervised and unsupervised learning to reconstruct conductivity distributions while ensuring adherence to the underlying physical laws. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to solve EIT problems using artificial intelligence. It combines two types of neural networks: Convolutional Neural Networks (CNNs) and Physics-Informed Neural Networks (PINNs). This combination helps solve EIT problems that are hard to solve because they rely on too much prior knowledge or assumptions. |
Keywords
» Artificial intelligence » Supervised » Unsupervised
