Summary of Comparing Analytic and Data-driven Approaches to Parameter Identifiability: a Power Systems Case Study, by Nikolaos Evangelou et al.
Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study
by Nikolaos Evangelou, Alexander M. Stankovic, Ioannis G. Kevrekidis, Mark K. Transtrum
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores parameter identifiability, a crucial aspect of machine learning that enables accurate inference of model parameters from observational data. Traditional approaches rely on analytical properties of the closed-form model to quantify responses to parameter variations. Data-driven techniques, specifically manifold learning methods like Output – Diffusion Maps and Geometric Harmonics, can complement or extend traditional analysis. This study compares and contrasts analytical and data-driven approaches for quantifying parameter identifiability and performing reduction tasks using the infinite bus synchronous generator model as a benchmark. The results show that both suites of tools (analytical and data-driven) yield comparable results, agreeing with traditional singular perturbation theory-based analysis. This work highlights the potential of data-driven methods in model analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to figure out what values make a mathematical model work correctly when we only have some information about it. Usually, experts use special tricks to do this, but new computer techniques can also help. The researchers tested these different methods using a well-known power systems model and found that they all give the same answers. This shows that these new computer techniques are useful for analyzing models like this one. |
Keywords
» Artificial intelligence » Diffusion » Inference » Machine learning » Manifold learning
