arXiv:2604.28167v2 Announce Type: replace-cross Abstract: The Vicsek model is a minimal model of collective motion, capturing how local alignment interactions can generate macroscopic nonequilibrium order in systems such as bird flocks. In this work, we use active learning to map the Vicsek phase diagram as a function of noise strength, density, and particle speed. A neural-network classifier is trained on global polar-order labels, and classifier entropy is used to select new simulations near uncertain crossover regions. The resulting phase map resolves a high-noise disordered gas, a low-nois
The continuous advancement in AI and machine learning techniques, particularly in active learning, is enabling more efficient exploration of complex scientific models.
This development showcases how AI can accelerate scientific discovery and parameter space exploration in fields beyond traditional AI applications, impacting materials science, robotics, and complex systems modeling.
The use of neural networks and active learning for mapping phase diagrams offers a more efficient methodology for scientific research, potentially reducing computational costs and time spent on simulations.
- · Materials scientists
- · Robotics researchers
- · AI/ML researchers
- · Computational modeling
- · Traditional brute-force simulation methods
More rapid discovery of optimal parameters for collective motion and self-organizing systems.
Application of similar AI-driven methodologies to other complex scientific domains, such as drug discovery or climate modeling.
Enhanced development of new materials and autonomous systems through accelerated understanding of their fundamental behaviors.
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Read at arXiv cs.LG