Summary of Invertible Fourier Neural Operators For Tackling Both Forward and Inverse Problems, by Da Long and Shandian Zhe
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems
by Da Long, Shandian Zhe
First submitted to arxiv on: 18 Feb 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 This paper proposes an invertible Fourier Neural Operator (iFNO) that can handle both forward prediction and inverse problems. Building upon the popular Fourier Neural Operator (FNO), iFNO leverages a series of invertible Fourier blocks to share parameters, exchange information, and regularize learning for bi-directional tasks. The model is further augmented with a variational auto-encoder to capture input structures and enable posterior inference. A three-step process for pre-training and fine-tuning allows for efficient training. Evaluations on five benchmark problems demonstrate the effectiveness of iFNO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to use a popular machine learning tool called Fourier Neural Operator (FNO). FNO is good at making predictions, but it’s not great at solving puzzles or finding answers. The new version, called invertible FNO, can do both! It uses special blocks that share information and help the model learn from doing both tasks together. This makes it better at handling problems where there isn’t enough data or there are lots of noises. The paper shows how to train this new model and tests it on five different problems. |
Keywords
* Artificial intelligence * Encoder * Fine tuning * Inference * Machine learning
