Summary of Physics and Geometry Informed Neural Operator Network with Application to Acoustic Scattering, by Siddharth Nair et al.
Physics and geometry informed neural operator network with application to acoustic scattering
by Siddharth Nair, Timothy F. Walsh, Greg Pickrell, Fabio Semperlotti
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS); 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 paper introduces a novel neural network architecture called DeepONet, which uses physics and geometry to predict the forward simulation of acoustic scattering for arbitrarily shaped scatterers. This is achieved through a geometric parameterization approach based on non-uniform rational B-splines (NURBS). The model learns a solution operator that can approximate physically-consistent scattered pressure fields in just a few seconds, improving computational time by orders of magnitude compared to traditional forward solvers. The paper also demonstrates the ability to evaluate scattered pressure fields without labeled training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to use computers to simulate how sound waves bounce off objects. It uses special math and computer code to make this happen. This means that scientists can now quickly figure out what would happen if they changed the shape of the object or moved it around, without having to do lots of complicated math problems. This is important because it can help us design better things like ships or buildings. |
Keywords
» Artificial intelligence » Neural network
