Summary of Automatically Learning Hybrid Digital Twins Of Dynamical Systems, by Samuel Holt and Tennison Liu and Mihaela Van Der Schaar
Automatically Learning Hybrid Digital Twins of Dynamical Systems
by Samuel Holt, Tennison Liu, Mihaela van der Schaar
First submitted to arxiv on: 31 Oct 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 Our paper introduces Hybrid Digital Twins (HDTwins), a novel approach to building digital twins that can generalize well in data-scarce settings. HDTwins combine mechanistic and neural components, leveraging domain knowledge and neural network expressiveness for enhanced generalization. We propose an evolutionary algorithm called HDTwinGen, which uses Large Language Models (LLMs) to automatically generate, evaluate, and optimize HDTwin specifications. Our results show that HDTwinGen produces sample-efficient, evolvable models with improved efficacy in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a computer program that can predict what will happen in the future based on how things are going right now. This is called a digital twin. But most of these programs don’t work well when they’re given new information they haven’t seen before. We want to change this by creating a special kind of digital twin that can learn and adapt quickly. To do this, we came up with an idea called HDTwins, which combines two different approaches to make the program more powerful. Then, we created a special algorithm called HDTwinGen that helps us find the best combination of these approaches. Our results show that our approach is better than existing methods and can be used in many real-world applications. |
Keywords
» Artificial intelligence » Generalization » Neural network
