Summary of Deep Learning For Model Correction Of Dynamical Systems with Data Scarcity, by Caroline Tatsuoka and Dongbin Xiu
Deep learning for model correction of dynamical systems with data scarcity
by Caroline Tatsuoka, Dongbin Xiu
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS); Machine Learning (stat.ML)
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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 introduces a deep learning framework for correcting existing dynamical system models using only a limited amount of high-fidelity data. The approach targets scenarios where high-resolution data is scarce, making it challenging to apply traditional model-driven methods. The authors first train a deep neural network (DNN) to approximate the existing low-fidelity model and then correct the DNN model via transfer learning (TL), leveraging the available high-fidelity data. This yields an improved DNN model with enhanced prediction accuracy for the underlying dynamics. The method does not assume a specific form of correction terms, instead offering an inherent correction to the low-fidelity model through TL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to improve models that are already somewhat accurate but lack high detail. Sometimes, we have limited amounts of very good data and want to use it to make our models better. The researchers developed a new way to do this by training a special kind of computer program (deep neural network) to mimic the existing model, then adjusting it using the scarce high-quality data. This results in a more accurate model that can predict the behavior of complex systems. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Transfer learning
