3 papers
cs.LG2026
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
Jack T. Beerman, Tyler J. Abele, Mehdi Taghizadeh +6
Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embedding differential operators direc…
cs.LG2026
Size is Not the Solution: Deformable Convolutions for Effective Physics Aware Deep Learning
Jack T. Beerman, Shobhan Roy, H. S. Udaykumar +1
Physics-aware deep learning (PADL) enables rapid prediction of complex physical systems, yet current convolutional neural network (CNN) architectures struggle with highly nonlinear…
cs.AI2024
Accelerating Hybrid Agent-Based Models and Fuzzy Cognitive Maps: How to Combine Agents who Think Alike?
Philippe J. Giabbanelli, Jack T. Beerman
While Agent-Based Models can create detailed artificial societies based on individual differences and local context, they can be computationally intensive. Modelers may offset thes…