Computer Science Speaking Skills Talk

  • Remote Access Enabled - Zoom
  • Virtual Presentation
Speaking Skills

Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction

Solving large complex partial differential equations (PDEs), such as those that arise in computational fluid dynamics (CFD), is a computationally expensive process. This has motivated the use of deep learning approaches to approximate the PDE solutions, yet the simulation results predicted from these approaches typically do not generalize well to truly novel scenarios. In this work, we develop a hybrid (graph) neural network that combines a graph convolutional network with an embedded differentiable fluid dynamics simulator inside the network itself. By combining an actual CFD simulator with the graph network, we show that we can both generalize well to new situations and benefit from the substantial speedup of neural network CFD predictions, while also substantially outperforming the coarse CFD simulation alone.

Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.

Zoom Participation. See announcement.

For More Information, Please Contact: