Summary of A Hybrid Approach Of Transfer Learning and Physics-informed Modeling: Improving Dissolved Oxygen Concentration Prediction in An Industrial Wastewater Treatment Plant, by Ece S. Koksal and Erdal Aydin
A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant
by Ece S. Koksal, Erdal Aydin
First submitted to arxiv on: 20 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Dynamical Systems (math.DS)
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 This paper explores the use of transfer learning to improve the prediction performance of a wastewater treatment plant. It proposes a novel approach that combines knowledge from open-source simulation models, noisy industrial data, and refined objective functions to achieve better results. By leveraging these different sources of information, the authors demonstrate significant improvements in test and validation performance, with gains of up to 27% and 59%, respectively. The paper showcases the potential of transfer learning for complex systems like wastewater treatment units. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new way to make better predictions for a big industrial machine that helps keep our water clean. Right now, making these predictions is hard because we don’t have all the information we need. This paper shows how to use knowledge from other places (like computer simulations and real-world data) to make better predictions. By combining this knowledge with new techniques, the authors were able to make their predictions much more accurate. |
Keywords
* Artificial intelligence * Transfer learning
