Summary of Multiple-input Fourier Neural Operator (mifno) For Source-dependent 3d Elastodynamics, by Fanny Lehmann et al.
Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics
by Fanny Lehmann, Filippo Gatti, Didier Clouteau
First submitted to arxiv on: 15 Apr 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 Multiple-Input Fourier Neural Operator (MIFNO) is a novel surrogate model that addresses the limitations of existing numerical solvers for simulating elastic wave propagation in complex 3D domains. By leveraging structured 3D fields and vectors describing source characteristics, MIFNO achieves good to excellent predictions based on Goodness-of-Fit (GOF) criteria, with high accuracy in wave arrival times, wave fronts’ propagation, and fluctuations amplitudes. The model demonstrates good generalization ability to new sources located outside the training domain and shows promising results when applied to a real complex overthrust geology. Furthermore, transfer learning improves accuracy with limited additional costs by leveraging specific samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The MIFNO is a new way to simulate earthquakes. It uses special kinds of pictures (3D fields) that show how materials in the Earth are different and special numbers (vectors) that describe where the earthquake starts. This helps it make very good predictions about what will happen when an earthquake happens. The model can even use information from other places to help predict what will happen, which makes it useful for predicting earthquakes in areas we don’t have as much data about. |
Keywords
» Artificial intelligence » Generalization » Transfer learning
