Summary of Knowledge-guided Machine Learning: Current Trends and Future Prospects, by Anuj Karpatne et al.
Knowledge-guided Machine Learning: Current Trends and Future Prospects
by Anuj Karpatne, Xiaowei Jia, Vipin Kumar
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 presents an overview of scientific modeling, highlighting the strengths and weaknesses of machine learning (ML) methods compared to process-based models. The authors introduce the emerging field of knowledge-guided machine learning (KGML), which combines scientific knowledge with ML frameworks for better generalizability, consistency, and explainability. They discuss various aspects of KGML research, including the types of scientific knowledge used, integration approaches, and methodological considerations. The paper also illustrates potential use cases in environmental sciences where KGML methods are being developed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make better models for science by combining what we know with machine learning. It compares different ways of doing this and shows examples of how it can be used in fields like environmental science. The main idea is that using both scientific knowledge and data can help us get more accurate and reliable results. |
Keywords
* Artificial intelligence * Machine learning
