activity
20202025
most citedRecent Advances on Machine Learning for Computational Fluid Dynamics: A Survey

30 citations · 71 across the 21 of their papers we have counts for

collaborators

23 papers

cs.LG2025

FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion

Fred Xu, Song Jiang, Zijie Huang +4

Taxonomy Expansion, which models complex concepts and their relations, can be formulated as a set representation learning task. The generalization of set, fuzzy set, incorporates u…

physics.flu-dyn2025

FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation

Haixin Wang, Ruoyan Li, Fred Xu +7

Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets a…

cs.CE2025

Self-Guided Diffusion Model for Accelerating Computational Fluid Dynamics

Ruoyan Li, Zijie Huang, Haixin Wang +3

Machine learning methods, such as diffusion models, are widely explored as a promising way to accelerate high-fidelity fluid dynamics computation via a super-resolution process fro…

cs.SI2025

A Social Dynamical System for Twitter Analysis

Zhiping Xiao, Xinyu Wang, Yifang Qin +3

Understanding the evolution of public opinion is crucial for informed decision-making in various domains, particularly public affairs. The rapid growth of social networks, such as…

cs.CL2025

Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection

Mingyu Derek Ma, Yanna Ding, Zijie Huang +3

Generative Language Models rely on autoregressive decoding to produce the output sequence token by token. Many tasks such as preference optimization, require the model to produce t…

cs.LG2025

Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation

Yanna Ding, Zijie Huang, Xiao Shou +3

Learning curve extrapolation predicts neural network performance from early training epochs and has been applied to accelerate AutoML, facilitating hyperparameter tuning and neural…