Presented By: Frontiers in Scientific Machine Learning (FSML)
Frontiers in Scientific Machine Learning Seminar 16: Conditional neural field latent diffusion model for generating spatiotemporal turbulence
Pan Du (University of Notre Dame)
Date: June 20, 2025, 12pm - 1pm
This is a hybrid event. To join via Zoom: Meeting ID: 978 2352 7756 (https://umich.zoom.us/j/97823527756)
Passcode: Enter last year in format YYYY
To join in person: 1642 GG Brown Building. Refreshments will be available.
Abstract:
Pan 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.
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.
This is a hybrid event. To join via Zoom: Meeting ID: 978 2352 7756 (https://umich.zoom.us/j/97823527756)
Passcode: Enter last year in format YYYY
To join in person: 1642 GG Brown Building. Refreshments will be available.
Abstract:
Pan 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.
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.
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