Researchers at Duke University have created a new artificial intelligence framework designed to uncover clear, easy-to-understand rules behind some of the most complicated dynamics seen in nature and modern technology.

The system is inspired by the work of history's great "dynamicists" -- scientists who study systems that change over time. Just as Isaac Newton, often considered the first dynamicist, developed equations linking force and motion, this AI analyzes data that shows how complex systems evolve and then produces equations that accurately describe that behavior.

What sets this approach apart is its ability to handle complexity far beyond human capacity. The AI can take nonlinear systems involving hundreds or even thousands of interacting variables and reduce them to simpler rules with far fewer dimensions.

A New Tool for Understanding Change Over Time

The research, published December 17 online in the journal npj Complexity, introduces a powerful new way for scientists to use AI to study systems that evolve over time -- including weather patterns, electrical circuits, mechanical devices, and biological signals.

"Scientific discovery has always depended on finding simplified representations of complicated processes," said Boyuan Chen, director of the General Robotics Lab and the Dickinson Family Assistant Professor of Mechanical Engineering and Materials Science at Duke. "We increasingly have the raw data needed to understand complex systems, but not the tools to turn that information into the kinds of simplified rules scientists rely on. Bridging that gap is essential."

A classic example of simplification comes from physics. The path of a cannon ball depends on many factors, including launch speed and angle, air resistance, changing wind conditions, and even ambient temperature. Despite this complexity, a close approximation of its motion can be captured with a simple linear equation that uses only the launch speed and angle.

Building on a Decades-Old Mathematical Idea

This kind of simplification reflects a theoretical concept introduced by mathematician Bernard Koopman in the 1930s. Koopman showed that complex nonlinear systems can be represented mathematically using linear models. The new AI framework builds directly on this idea.

There is an important challenge, however. Representing highly complex systems with linear models often requires constructing hundreds or even thousands of equations, each tied to a different variable. Handling that level of complexity is difficult for human researchers.

That is where artificial intelligence becomes especially valuable.

How the AI Reduces Complexity

The framework studies time-series data from experiments and identifies the most meaningful patterns in how a system changes. It combines deep learning with constraints inspired by physics to narrow down the system to a much smaller set of variables that still capture its essential behavior. The outcome is a compact model that behaves mathematically like a linear system while remaining faithful to real-world complexity.