Researchers have developed a new artificial intelligence framework capable of learning the fundamental, local rules that govern the collective motion of groups, such as bird flocks or fish schools. By integrating known physical principles directly into the AI’s training process, the model can decipher how simple individual interactions lead to complex, organized group behaviors. The breakthrough provides a powerful tool for understanding emergent phenomena in nature and designing controlled multi-agent systems.
A team from Seoul National University and Kyung Hee University reported the new physics-informed AI in the journal Cell Reports Physical Science. Their approach successfully reverse-engineers the rules of interaction from observed group patterns. Unlike traditional data-driven models that rely solely on vast amounts of information, this method embeds governing equations into the machine learning process. This allows the AI to generalize effectively from limited data and ensures its predictions remain consistent with the laws of physics, a crucial step in moving from random individual movements to ordered, collective states like swarms, mills, and rings.
Decoding Nature’s Algorithms
The study of collective motion has long fascinated scientists. From the mesmerizing aerial ballets of starling murmurations to the coordinated hunting patterns of fish schools, nature is filled with examples of complex, large-scale order emerging from simple, localized interactions among individuals. For decades, the central challenge has been to identify these precise local rules. Researchers understood that individuals were not following a leader or a master plan but were reacting to the positions and orientations of their immediate neighbors. Pinpointing the exact rules of attraction, repulsion, and alignment that produce these behaviors has proven exceptionally difficult.
Traditional methods involved observing natural systems and attempting to formulate mathematical models that could replicate the observed patterns. However, these models often struggled to capture the full diversity and adaptability of real-world collective behaviors. The sheer number of agents and the complexity of their interactions created a puzzle that was difficult to solve from observation alone. The inverse problem—deducing the underlying rules from the resulting group behavior—remained a significant hurdle in biology and physics.
A Physics-Informed Approach
The new research introduces a powerful solution by using a class of models often called Physics-Informed Neural Networks (PINNs). These systems bridge the gap between pure data-driven machine learning and physical science. Instead of treating the AI as a “black box” that simply finds patterns in data, researchers embed known physical laws or governing equations directly into the model’s learning algorithm. This infusion of domain knowledge acts as a strong constraint, guiding the AI to find solutions that are not only statistically plausible but also physically realistic.
In this study, the team designed the AI to learn the interaction rules that could successfully generate specific collective patterns. The system starts with individuals in random initial conditions and, by adjusting the interaction parameters, learns what rules are necessary to make the group spontaneously organize into a desired state, such as a rotating ring, a compact clump, or a cohesive flock. This allows the framework to specify the precise conditions under which order emerges from chaos and to control the geometric features of the resulting group, like its size and radius.
Validating the Model with Real-World Data
To test their framework’s applicability to natural systems, the researchers applied it to a real-world dataset: GPS trajectories from pigeon flocks. By training the physics-informed AI on this data, the model successfully uncovered the interaction mechanisms that real pigeons use to fly together. This demonstrated the system’s ability to extract meaningful biological principles from observational data, confirming its value as a tool for scientific discovery.
This validation is a critical step, showing the model can move beyond theoretical simulations and provide genuine insights into the behaviors of living creatures. The ability to decipher these rules from trajectory data opens up new possibilities for studying animal behavior without invasive experiments. It suggests that similar methods could be used to analyze the movements of other species, from insect swarms to mammal herds, providing a new window into the hidden algorithms that govern the natural world.
The Advantage of Integrated Physics
Robustness with Less Data
One of the primary advantages of physics-informed AI is its ability to perform well even with limited or sparse data. Traditional deep learning models often require massive datasets to learn underlying patterns, and their predictions can sometimes be physically nonsensical if they encounter situations outside their training domain. By incorporating physical laws, the model has a built-in “understanding” of how the world works. This reduces its reliance on data alone and helps it generalize better to new scenarios, a key feature for studying complex systems where comprehensive data is hard to obtain.
Enhanced Interpretability
Furthermore, this approach can lead to more interpretable results. Rather than just creating a model that can predict a flock’s movement, the framework identifies the specific interaction rules that drive the behavior. This focus on uncovering the governing equations provides scientists with understandable, symbolic representations of a system’s dynamics. It moves beyond simple prediction and toward genuine scientific understanding, helping researchers formulate and test new hypotheses about how collective systems function.
Future Applications and Scientific Horizons
The implications of this research extend far beyond the study of animal behavior. The ability to design and control the collective motion of multiple agents has significant potential in robotics and engineering. For example, the same principles could be used to program swarms of autonomous drones for search-and-rescue operations, environmental monitoring, or coordinated transport tasks. By defining the desired collective outcome, the AI could generate the simple, local rules needed to guide the individual robots, ensuring robust and scalable control without complex centralized command.
In the broader scientific landscape, this work represents a step toward a new paradigm of automated scientific discovery. As these AI models become more sophisticated, they could be applied to systems where the governing equations are not well understood or are yet to be discovered. By feeding observational data into a physics-informed framework, researchers may one day be able to uncover new fundamental laws in fields ranging from fluid dynamics to cosmology, accelerating the pace of discovery and deepening our understanding of the universe.