5 papers
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…
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…
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…
Toolpath Generation for High Density Spatial Fiber Printing Guided by Principal Stresses
Tianyu Zhang, Tao Liu, Neelotpal Dutta +5
While multi-axis 3D printing can align continuous fibers along principal stresses in continuous fiber-reinforced thermoplastic (CFRTP) composites to enhance mechanical strength, ex…
Exceptional Mechanical Performance by Spatial Printing with Continuous Fiber: Curved Slicing, Toolpath Generation and Physical Verification
Guoxin Fang, Tianyu Zhang, Yuming Huang +3
This work explores a spatial printing method to fabricate continuous fiber-reinforced thermoplastic composites (CFRTPCs), which can achieve exceptional mechanical performance. For…