Summary of Estimate Epidemiological Parameters Given Partial Observations Based on Algebraically Observable Pinns, by Mizuka Komatsu
Estimate Epidemiological Parameters given Partial Observations based on Algebraically Observable PINNs
by Mizuka Komatsu
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS); Populations and Evolution (q-bio.PE)
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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 A novel approach to estimating epidemiological parameters using physics-informed neural networks (PINNs) is presented. The challenge lies in utilizing partial observations with noise, where not all trajectory data is available. To address this, the concept of algebraic observability is integrated into PINNs. The proposed method, an algebraically observable PINN, is validated through numerical experiments, showcasing its effectiveness in estimating unknown parameters and predicting unobserved variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed a new way to estimate how diseases spread using special types of artificial intelligence called physics-informed neural networks (PINNs). One problem with this approach is that not all the data needed to make predictions is available. Some information is missing or noisy. To solve this issue, researchers introduced an idea called algebraic observability into PINNs. This new method was tested and shown to be successful in estimating unknown values and making accurate predictions. |
