12pm – 1pm
Wilkinson Building, room 021 auditorium
For centuries, researchers have sought out ways to connect disparate areas of knowledge. With the advent of Artificial Intelligence (AI), we can now rigorously explore relationships that span across distinct areas – such as, mechanics and biology, or science and art – to deepen our understanding, to accelerate innovation, and to drive scientific discovery. However, many existing AI methods have limitations when it comes to physical intuition, and often hallucinate. To address these challenges, we present research that blurs the boundary between physics-based and data-driven modeling through a series of physics-inspired multimodal graph-based generative AI models, set forth in a hierarchical multi-agent mixture-of-experts framework. The design of these models follows a biologically inspired approach where we re-use neural structures and dynamically arrange them in different patterns and utility, implementing a manifestation of the universality-diversity-principle that forms a powerful principle in bioinspired materials.
