37 citations · 62 across the 5 of their papers we have counts for
5 papers
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…
Automatic differentiation accelerated shape optimization approaches to photonic inverse design on rectilinear simulation grids
Sean Hooten, Peng Sun, Liron Gantz +3
Shape optimization approaches to inverse design offer low-dimensional, physically-guided parameterizations of structures by representing them as combinations of shape primitives. H…
Adjoint optimization of polarization-splitting grating couplers
Peng Sun, Thomas Van Vaerenbergh, Sean Hooten +1
We have designed a polarization-splitting grating coupler (PSGC) in silicon-oninsulator (SOI) that has 1.2 dB peak loss in numerical simulations, which is the best simulated perfor…
Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning
Sean Hooten, Raymond G. Beausoleil, Thomas Van Vaerenbergh
We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic…