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Bio: Pan Du received his bachelor’s degree in Thermal Engineering from Tsinghua University and completed his master’s in Mechanical Engineering at Washington University in St. Louis. He is currently a Ph.D. candidate in Aerospace and Mechanical Engineering at the University of Notre Dame under the guidance of Prof. Jian-Xun Wang. Pan’s research spans multiple disciplines, including scientific machine learning, Bayesian inference, uncertainty quantification, geometric deep learning, and computational fluid mechanics.

Conditional neural field latent diffusion model for generating spatiotemporal turbulence

Abstract: Pan Du will present the CoNFiLD model, a novel generative framework for simulating complex turbulent flows in 3D irregular domains. While traditional eddy-resolved simulations are accurate, their high computational cost limits usability. CoNFiLD addresses this by integrating neural field encoding with latent diffusion, enabling efficient, probabilistic modeling of spatiotemporal dynamics. It supports a wide range of tasks—such as flow super-resolution, sparse reconstruction, and data restoration—via Bayesian conditional sampling, all without retraining. Results across diverse turbulent scenarios highlight its potential for advancing data-driven turbulence modeling.