Modeling Physical Structure and Dynamics Using Graph-Based Machine Learning

Explore graph-based machine learning techniques for modeling physical structures and dynamics in this IEEE Signal Processing Society webinar. Delve into the rich structure of datasets and learn about graphical networks, algorithm explanations, and model architectures. Discover various simulations including sand, goop, particle, and multiple materials. Examine research on rigid materials, mesh-based systems, and compressible/incompressible fluids. Investigate generalization experiments, system and chemical Polygem, construction species, and the Silhouette Task. Compare absolute vs. relative action and edge-based relative agent results. Gain insights from Peter Battaglia of Deepmind in this comprehensive exploration of graph-based approaches to physical modeling.

Introduction
Datasets are richly structured
What tool do I need
Outline the purpose
Background on graphical networks
Algorithm explanation
Model overview
Architectures
Research
Round truth simulation
Sand simulation
Goop simulation
Particle simulation
Multiple materials
Graphical networks
Rigid materials
Meshbased systems
Measuring accuracy
Compressible incompressible fluids
Generalization experiments
System Polygem
Chemical Polygem
Construction Species
Silhouette Task
Absolute vs Relative Action
Edgebased Relative Agent
Results
Conclusions
Questions