30 citations · 32 across the 8 of their papers we have counts for
8 papers
Unlocking Reasoning Capabilities in LLMs via Reinforcement Learning Exploration
Wenhao Deng, Long Wei, Chenglei Yu +1
Reinforcement learning with verifiable rewards (RLVR) has recently enhanced the reasoning capabilities of large language models (LLMs), particularly for mathematical problem solvin…
BuildArena: A Physics-Aligned Interactive Benchmark of LLMs for Engineering Construction
Tian Xia, Tianrun Gao, Wenhao Deng +4
Engineering construction automation aims to transform natural language specifications into physically viable structures, requiring complex integrated reasoning under strict physica…
From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control
Peiyan Hu, Xiaowei Qian, Wenhao Deng +8
The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requi…
Wavelet Diffusion Neural Operator
Peiyan Hu, Rui Wang, Xiang Zheng +7
Simulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative mode…
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…
CL-DiffPhyCon: Closed-loop Diffusion Control of Complex Physical Systems
Long Wei, Haodong Feng, Yuchen Yang +6
The control problems of complex physical systems have broad applications in science and engineering. Previous studies have shown that generative control methods based on diffusion…
DiffPhyCon: A Generative Approach to Control Complex Physical Systems
Long Wei, Peiyan Hu, Ruiqi Feng +7
Controlling the evolution of complex physical systems is a fundamental task across science and engineering. Classical techniques suffer from limited applicability or huge computati…
Compositional Generative Inverse Design
Tailin Wu, Takashi Maruyama, Long Wei +4
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem that arises across fields such as mechanical…