Summary of Pyawd: a Library For Generating Large Synthetic Datasets Of Acoustic Wave Propagation with Devito, by Pascal Tribel et al.
PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation with Devito
by Pascal Tribel, Gianluca Bontempi
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, a new Python library called PyAWD is introduced to generate synthetic datasets simulating acoustic wave propagation in various media. The goal is to enable machine learning (ML) applications in earthquake analysis despite the limitations imposed by sparsely distributed and unevenly distributed seismic data. By providing fine control over parameters such as wave speed, external forces, and spatial and temporal discretization, PyAWD allows for the creation of ML-scale datasets that capture the complexity of seismic wave behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PyAWD is a tool designed to generate high-resolution synthetic datasets simulating acoustic waves in different types of media. This can help with machine learning tasks like earthquake analysis, where real-world data might be limited due to costs and logistical challenges. The library lets you control various parameters to create datasets that are suitable for ML. |
Keywords
* Artificial intelligence * Machine learning
