Researchers develop AI tool that finds the equations behind complex systems
tags:
Clarkson University researchers have developed an artificial intelligence tool that can uncover the mathematical equations governing complex and chaotic systems directly from data. The technology, called KANDy—short for Kolmogorov-Arnold Networks for Dynamics—is designed to help scientists understand systems that are difficult to describe using traditional methods because they are noisy, nonlinear or highly unpredictable.
Most AI models excel at making predictions but often operate as "black boxes," offering little insight into why they behave as they do. KANDy takes a different approach. Rather than just providing predictions of future actions, KANDy aims to understand the equations governing the phenomenon.
Researchers can feed KANDy data from a complex physical system, and the model attempts to identify the mathematical rules driving that system's behavior. The result is an AI model that is both predictive and interpretable.
The new framework builds on a class of neural networks known as Kolmogorov-Arnold Networks, or KANs. By adapting the technology specifically for dynamical systems, the researchers created a model capable of discovering governing equations even in cases where existing equation-discovery methods fail.
The study, now available on the arXiv preprint server, was conducted by Research Associate Kevin Slote and Electrical and Computer Engineering Research Assistant Professor Jeremie Fish, led by Erik Bollt, who tested KANDy on a variety of challenging problems, including discrete and continuous dynamical systems and chaotic partial differential equations.
The model also successfully recovered important topological structure in a mathematical object known as the Hopf fibration, demonstrating its ability to capture deeper properties of complex systems.
The research highlights KANDy's potential for data-driven modeling of nonlinear dynamical systems, providing scientists and engineers with a new tool for understanding complicated physical phenomena from observed data.
To install the KANDy software and try it, see the installation instructions on GitHub.
Publication details
Kevin Slote et al, KANDy: Kolmogorov-Arnold Networks and Dynamical System Discovery, arXiv (2026). DOI: 10.48550/arxiv.2602.20413
Who's behind this story?
BA art history, MA material culture. Former museum editor, paramedic, and transplant coordinator. Editing for Science X since 2021. Full profile →
Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →
Citation: Researchers develop AI tool that finds the equations behind complex systems (2026, July 7) retrieved 14 July 2026 from https://phys.org/news/2026-07-ai-tool-equations-complex.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.