Summary of Deep Generative Modeling For Identification Of Noisy, Non-stationary Dynamical Systems, by Doris Voina and Steven Brunton and J. Nathan Kutz
Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems
by Doris Voina, Steven Brunton, J. Nathan Kutz
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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 A machine learning approach is proposed for data-driven system identification of nonlinear, noisy, and non-autonomous dynamical systems. The method, dynamic SINDy, combines variational inference with SINDy (sparse identification of nonlinear dynamics) to model time-varying coefficients of sparse ODEs. This framework allows for uncertainty quantification of ODE coefficients, expanding on previous methods for autonomous systems. The approach is validated using synthetic data, including nonlinear oscillators and the Lorenz system, and applied to neuronal activity data from C. elegans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how things change over time is being developed. Scientists are trying to figure out how to make sense of messy data that shows how things move or change in different ways at different times. They’ve created a special method called dynamic SINDy that helps them do this by finding the simplest possible equations that explain what’s happening. This method can handle noisy and chaotic data, which is really important for understanding many natural systems. |
Keywords
» Artificial intelligence » Inference » Machine learning » Synthetic data
