5 papers
Therm-FM: Foundation Model is ALL YOU NEED for 3D-ICs Thermal Simulation
Zhen Huang, Haiyang Xin, Wenkai Yang +6
Data-driven thermal predictors for 3D-ICs are often trained from scratch for each chip design using many high-fidelity finite-element simulations, leading to high data-generation c…
Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
Zherui Yang, Haiyang Xin, Tao Du +1
Neural operators have emerged as data-driven surrogates for solving partial differential equations (PDEs), and their success hinges on efficiently modeling the long-range, global c…
Accelerating Data Generation for Nonlinear temporal PDEs via homologous perturbation in solution space
Lei Liu, Zhenxin Huang, Hong Wang +4
Data-driven deep learning methods like neural operators have advanced in solving nonlinear temporal partial differential equations (PDEs). However, these methods require large quan…
From Uniform to Adaptive: General Skip-Block Mechanisms for Efficient PDE Neural Operators
Lei Liu, Zhongyi Yu, Hong Wang +4
In recent years, Neural Operators(NO) have gradually emerged as a popular approach for solving Partial Differential Equations (PDEs). However, their application to large-scale engi…
Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Training
Hong Wang, Haiyang Xin, Jie Wang +4
Pre-training has proven effective in addressing data scarcity and performance limitations in solving PDE problems with neural operators. However, challenges remain due to the heter…