Summary of A Novel Framework For Generalization Of Deep Hidden Physics Models, by Vijay Kag et al.
A novel framework for generalization of deep hidden physics models
by Vijay Kag, Birupaksha Pal
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Analysis of PDEs (math.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle a common challenge in engineering and industry: modeling complex systems where full information is unknown. Traditional approaches often rely on simplifications or assumptions to keep models manageable. Recent advances in greybox modeling, such as deep hidden physics models, combine data and physics to improve accuracy. However, model generalizability remains a major issue, as retraining a model for every small change can be impractical. The authors propose a novel enhancement to hidden physics models that can generalize to changes in system inputs, parameters, and domains. They demonstrate the effectiveness of this approach not only for modeling but also for system discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can create better models of complex systems when we don’t have all the information we need. Right now, engineers often have to make simplifying assumptions or simplify their models just to keep them manageable. New techniques called deep hidden physics models are trying to solve this problem by combining data and physical rules. But there’s still a big challenge: how do we get our model to work well when things change? The authors of this paper propose a new way to make these models more flexible, so they can adapt to changes in the system or its inputs. This is important not just for modeling but also for discovering new systems and understanding their behavior. |
