My current research topic focuses on leveraging the Denoising Diffusion Probabilistic Model (DDPM) for physical applications. DDPM has emerged as a state-of-the-art generative model, demonstrating superior performance in synthesizing impressive results across various domains. Exploring its application in physics research represents a burgeoning area with significant potential. My study emphasizes utilizing DDPM’s capability to reconstruct diverse probability distributions accurately for studying physical problems involving uncertainty. Meanwhile, by integrating existing physical knowledge into the training of the diffusion model, I aim to elevate DDPM into a cutting-edge generative model for complex physical systems. Moreover, I am also interested in topics such as differentiable simulations, physics-informed neural networks, and advanced technologies computational fluid simulations.
I have been a Ph.D. student in Nils Thuerey’s group since October 2022.
Contact
E-mail: qiang7.liu (at) tum.de
Room: 02.13.039
Publications
- ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks, Qiang Liu, Mengyu Chu, and Nils Thuerey, Arxiv,2024 [Project]
-
Uncertainty-Aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models, Qiang Liu and Nils Thuerey, AIAA Journal 2024 62:8, 2912-2933 [Project]