Summary of Modeling Unknown Stochastic Dynamical System Subject to External Excitation, by Yuan Chen et al.
Modeling Unknown Stochastic Dynamical System Subject to External Excitation
by Yuan Chen, Dongbin Xiu
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Dynamical Systems (math.DS)
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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 This paper presents a novel numerical method for learning unknown non-autonomous stochastic dynamical systems. The approach assumes that the governing equations are unavailable, but short bursts of input/output data consisting of known excitation signals and corresponding system responses are available. When sufficient training data is provided, the method learns the unknown dynamics and produces an accurate predictive model for arbitrary excitation signals not in the training data. The method consists of two key components: local approximation of the training data to transfer learning into a parameterized form and generative modeling to approximate the underlying unknown stochastic flow map in distribution. The paper presents comprehensive numerical examples demonstrating the performance of the proposed method, particularly for long-term system predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to learn about complex systems that are affected by changing external factors. Imagine trying to predict how a spinning top will move when you’re only given a few glimpses of its behavior. That’s basically what this paper is all about. The researchers created a method that can learn about these types of systems even if we don’t know the underlying rules. They do this by looking at short bursts of data and then using that information to make predictions for future situations. This has important implications for things like weather forecasting, traffic management, or predicting how animals will behave in different environments. |
Keywords
* Artificial intelligence * Transfer learning
