Summary of Discovery and Inversion Of the Viscoelastic Wave Equation in Inhomogeneous Media, by Su Chen et al.
Discovery and inversion of the viscoelastic wave equation in inhomogeneous media
by Su Chen, Yi Ding, Hiroe Miyake, Xiaojun Li
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Geophysics (physics.geo-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 hybrid framework combines sparse regression techniques and a recurrent convolutional neural network (RCNN) to identify partial differential equations accurately from noisy and sparse data. The method first uses current sparse regression methods to preliminarily identify governing equations, then optimizes the results using an RCNN model that implements time-space iterations for wave equation discretization. This alternating update process allows for robust identification of essential physical equations even in high-noise environments with limited data availability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to find important mathematical formulas is developed by combining two different approaches. First, it uses a method called sparse regression to get close to the correct answer. Then, it uses a special kind of neural network called RCNN to make small adjustments and get even closer to the true answer. This process helps scientists figure out important equations that are hidden in noisy data. |
Keywords
» Artificial intelligence » Neural network » Rcnn » Regression
