30 citations · 34 across the 8 of their papers we have counts for
8 papers · 1 filter
Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models
Shashank Sule, Xinyue Yu, Boonam Shin +3
Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling. In this paper, we examine this question from t…
AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates
Minxin Zhang, Yuxuan Liu, Hayden Schaeffer
The recently proposed Muon optimizer updates weight matrices via orthogonalized momentum and has demonstrated strong empirical success in large language model training. However, it…
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions
Elisa Negrini, Yuxuan Liu, Liu Yang +2
Neural networks are one tool for approximating non-linear differential equations used in scientific computing tasks such as surrogate modeling, real-time predictions, and optimal c…
BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics
Yuxuan Liu, Jingmin Sun, Hayden Schaeffer
We introduce BCAT, a PDE foundation model designed for autoregressive prediction of solutions to two dimensional fluid dynamics problems. Our approach uses a block causal transform…
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
Yadi Cao, Yuxuan Liu, Liu Yang +3
In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, ex…
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
Yuxuan Liu, Jingmin Sun, Xinjie He +3
We propose PROSE-FD, a zero-shot multimodal PDE foundational model for simultaneous prediction of heterogeneous two-dimensional physical systems related to distinct fluid dynamics…
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
Haixin Wang, Yadi Cao, Zijie Huang +13
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques. We begin by introducing fundamental conc…
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
Jingmin Sun, Yuxuan Liu, Zecheng Zhang +1
Foundation models, such as large language models, have demonstrated success in addressing various language and image processing tasks. In this work, we introduce a multi-modal foun…