6 papers · 1 filter
ProFlow: Zero-Shot Physics-Consistent Sampling via Proximal Flow Guidance
Zichao Yu, Ming Li, Wenyi Zhang +2
Inferring physical fields from sparse observations while strictly satisfying partial differential equations (PDEs) is a fundamental challenge in computational physics. Recently, de…
F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning
Hangwei Zhang, Chun Kang, Yan Wang +1
Parameter-efficient fine-tuning (PEFT) of powerful pre-trained models for complex downstream tasks has proven effective in vision and language processing, yet this paradigm remains…
Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory
Hanru Bai, Weiyang Ding, Difan Zou
Diffusion models have achieved impressive success in high-fidelity image generation but suffer from slow sampling due to their inherently iterative denoising process. While recent…
On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models
Yi Zhang, Zhenyu Liao, Jingfeng Wu +1
Despite the widespread adoption of deterministic samplers in diffusion models (DMs), their potential limitations remain largely unexplored. In this paper, we identify collapse erro…
Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation
Yi Zhang, Peng Wang, Difan Zou
Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate diverse solutions, and address both forwa…
How Does Critical Batch Size Scale in Pre-training?
Hanlin Zhang, Depen Morwani, Nikhil Vyas +5
Training large-scale models under given resources requires careful design of parallelism strategies. In particular, the efficiency notion of critical batch size (CBS), concerning t…