Summary of Adam-sindy: An Efficient Optimization Framework For Parameterized Nonlinear Dynamical System Identification, by Siva Viknesh et al.
ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification
by Siva Viknesh, Younes Tatari, Amirhossein Arzani
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper introduces a novel method within the Sparse Identification of Nonlinear Dynamics (SINDy) framework, called ADAM-SINDy. This approach combines the strengths of established methods by employing the ADAM optimization algorithm. The goal is to precisely estimate nonlinear parameters and coefficients without requiring prior knowledge of nonlinear characteristics. The method uses an integrated global optimization to adjust all unknown variables in response to data, reducing sensitivity to candidate functions. The performance is demonstrated across various dynamical systems, including benchmark coupled nonlinear ordinary differential equations and nonlinear partial differential equations. The results show significant improvements in identifying parameterized dynamical systems, highlighting the potential of ADAM-SINDy to extend the SINDy framework’s applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to find mathematical models for complex things that move and change over time. It combines two old methods to make something better. This new method looks at data and adjusts its guesses about the rules that govern how things move. It does this without knowing ahead of time what kind of math should be used. The test results show that this new way is good at finding models for many different kinds of systems. |
Keywords
* Artificial intelligence * Optimization
