AI/ML+Physics Part 3: Designing an Architecture

Graph neural networks have produced impressive results in machine learning for physical systems. These networks have been used to discover laws of planetary motion that can be applied to multi-planet systems and simulate fluid flows. GNNS are designed to incorporate assumptions about the structure of interactions, such as end body systems, molecular dynamics, or rigid body systems. By integrating physics into these networks, there are numerous opportunities for advancement.

I am eager to learn more about this topic and plan to explore it further. This will allow us to delve into the topic together and understand the powerful demonstrations of efficient and accurate machine learning models in simulating complex physics. One remarkable paper demonstrates the ability to simulate various fluids, elastics, and complicated partial differential equations using simple concepts in Graph neural networks to incorporate the physics of the system.