3 citations · 3 across the 5 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving
Chang Su, Zhongkai Hao, Zhizhou Zhang +4
Large language models (LLMs) with reasoning abilities have demonstrated growing promise for tackling complex scientific problems. Yet such tasks are inherently domain-specific, unb…
cs.LG2025
From Cheap Geometry to Expensive Physics: Elevating Neural Operators via Latent Shape Pretraining
Zhizhou Zhang, Youjia Wu, Kaixuan Zhang +1
Industrial design evaluation often relies on high-fidelity simulations of governing partial differential equations (PDEs). While accurate, these simulations are computationally exp…
cs.LG2025
Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
Ze Cheng, Zhuoyu Li, Xiaoqiang Wang +4
PDE-Constrained Optimization (PDECO) problems can be accelerated significantly by employing gradient-based methods with surrogate models like neural operators compared to tradition…