Summary of Expressive Symbolic Regression For Interpretable Models Of Discrete-time Dynamical Systems, by Adarsh Iyer et al.
Expressive Symbolic Regression for Interpretable Models of Discrete-Time Dynamical Systems
by Adarsh Iyer, Nibodh Boddupalli, Jeff Moehlis
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Symbolic Computation (cs.SC)
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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 A machine learning architecture called Symbolic Artificial Neural Network-Trained Expressions (SymANNTEx) is proposed to identify mathematical expressions defining discrete-time dynamical systems, also known as iterated maps, given only their data streams. The modified SymANNTEx model pipeline optimizes the regression process and characterizes its behavior in identifying classical chaotic maps with single-state and dual-state attractors. Sparsity-inducing weight regularization and information theory-informed simplification are implemented to achieve parsimony. The proposed method shows promise for data-driven scientific discovery and interpretation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to find mathematical rules that describe how systems change over time, using only the data from those systems. This helps us understand complex behaviors better. They created a special kind of computer program called SymANNTEx that can figure out these rules given just the data. The program was tested on some well-known examples and showed promise for finding simple and accurate descriptions of how systems work. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Regression » Regularization
