Summary of Generating Synthetic Data For Neural Operators, by Erisa Hasani et al.
Generating synthetic data for neural operators
by Erisa Hasani, Rachel A. Ward
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 In this paper, researchers propose an innovative approach to generating synthetic training data for deep learning models solving partial differential equations (PDEs). Traditionally, these models rely on numerical solvers like finite difference or finite element methods. The proposed method draws random functions from the underlying solution space and plugs them into the PDE equation, producing corresponding right-hand side functions. This “backwards” approach leverages derivative computations to generate numerous data points quickly and efficiently. By eliminating the need for classical numerical solvers, this method expands the potential for developing neural PDE solvers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed approach allows for generating synthetic functional training data that doesn’t require solving a PDE numerically. It draws random functions from the underlying solution space and plugs them into the equation, producing corresponding right-hand side functions. This “backwards” method uses derivative computations to generate many data points quickly and efficiently. |
Keywords
* Artificial intelligence * Deep learning
