121 citations · 150 across the 14 of their papers we have counts for
15 papers
DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design
Xinling Yu, Yixing Li, Ziyue Liu +4
Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve. Operator-learning surrogates replace this solve with a fas…
GRZO: Group-Relative Zeroth-Order Optimization for Large Language Model Fine-Tuning
Liyan Tan, Yequan Zhao, Yifan Yang +3
Zeroth-order (ZO) optimization is a memory-efficient alternative to backpropagation for fine-tuning large language models, but its deployment is limited by the high variance of gra…
KANO: Kolmogorov-Arnold Neural Operator
Jin Lee, Ziming Liu, Xinling Yu +4
We introduce Kolmogorov--Arnold Neural Operator (KANO), a dual-domain neural operator jointly parameterized by both spectral and spatial bases with intrinsic symbolic interpretabil…
DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design
Xinling Yu, Ziyue Liu, Hai Li +5
Thermal analysis is crucial in 3D-IC design due to increased power density and complex heat dissipation paths. Although operator learning frameworks such as DeepOHeat~\cite{liu2023…
Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks
Yequan Zhao, Xinling Yu, Xian Xiao +6
Physics intelligence and digital twins often require rapid and repeated performance evaluation of various engineering systems (e.g. robots, autonomous vehicles, semiconductor chips…
Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
Yequan Zhao, Xian Xiao, Antoine Descos +6
Partial differential equation (PDE) is an important math tool in science and engineering. This paper experimentally demonstrates an optical neural PDE solver by leveraging the back…
Photonic KAN: a Kolmogorov-Arnold network inspired efficient photonic neuromorphic architecture
Yiwei Peng, Sean Hooten, Xinling Yu +7
Kolmogorov-Arnold Networks (KAN) models were recently proposed and claimed to provide improved parameter scaling and interpretability compared to conventional multilayer perceptron…
Separable Operator Networks
Xinling Yu, Sean Hooten, Ziyue Liu +4
Operator learning has become a powerful tool in machine learning for modeling complex physical systems governed by partial differential equations (PDEs). Although Deep Operator Net…
Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training
Yequan Zhao, Xian Xiao, Xinling Yu +5
Solving partial differential equations (PDEs) numerically often requires huge computing time, energy cost, and hardware resources in practical applications. This has limited their…
Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks
Yequan Zhao, Xinling Yu, Zhixiong Chen +3
Backward propagation (BP) is widely used to compute the gradients in neural network training. However, it is hard to implement BP on edge devices due to the lack of hardware and so…
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design
Ziyue Liu, Yixing Li, Jing Hu +5
Thermal issue is a major concern in 3D integrated circuit (IC) design. Thermal optimization of 3D IC often requires massive expensive PDE simulations. Neural network-based thermal…
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
Xinling Yu, José E. C. Serrallés, Ilias I. Giannakopoulos +4
We propose Physics-Informed Fourier Networks for Electrical Properties (EP) Tomography (PIFON-EPT), a novel deep learning-based method for EP reconstruction using noisy and/or inco…
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Xinling Yu, José E. C. Serrallés, Ilias I. Giannakopoulos +4
Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP can be potential biomark…
TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing
Ziyue Liu, Xinling Yu, Zheng Zhang
Physics-informed neural networks (PINNs) have been increasingly employed due to their capability of modeling complex physics systems. To achieve better expressiveness, increasingly…
When and why PINNs fail to train: A neural tangent kernel perspective
Sifan Wang, Xinling Yu, Paris Perdikaris
Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial…