Summary of Hybrid Modeling Design Patterns, by Maja Rudolph et al.
Hybrid Modeling Design Patterns
by Maja Rudolph, Stefan Kurz, Barbara Rakitsch
First submitted to arxiv on: 29 Dec 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes four design patterns for hybrid modeling, which combines first-principle-based modeling with data-driven techniques. The approach aims to provide a systematic solution to recurring challenges in modeling. By combining domain knowledge with data-driven components, the hybrid models can leverage the strengths of both approaches. The authors introduce two composition patterns that govern the combination of these base patterns into more complex models. Typical use cases from fields like climate modeling, engineering, and physics illustrate each design pattern. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hybrid modeling is a new approach that combines different ways of thinking to solve problems. Imagine you’re trying to predict the weather or understand how cars move. Usually, scientists use one way or another to get answers, but what if you could mix them together? That’s what this paper does! It gives you four basic recipes (design patterns) and two ways to combine those recipes into something new. You’ll see examples from real-life applications like studying the climate or designing engines. |
