Benchmarking five global optimization approaches for nano-optical shape
optimization and parameter reconstruction

Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction

18 September 2018

Philipp‐Immanuel Schneider

Xavier Garcia Santiago

M. Hammerschmidt

Papers citing "Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction"

10 / 10 papers shown

Title
Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions Matthias Plock M. Hammerschmidt Sven Burger Philipp‐Immanuel Schneider Christof Schütte 16 0 0 04 May 2023
A neural operator-based surrogate solver for free-form electromagnetic inverse design Yannick Augenstein T. Repän C. Rockstuhl AI4CE 24 28 0 04 Feb 2023
Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction Matthias Plock Kas Andrle Sven Burger Philipp‐Immanuel Schneider 14 5 0 23 Feb 2022
Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology Matthias Plock Sven Burger Philipp‐Immanuel Schneider 13 4 0 12 Jul 2021
A Framework for Discovering Optimal Solutions in Photonic Inverse Design Jagrit Digani Phillip Hon Artur R. Davoyan 12 0 0 03 Jun 2021
Bayesian optimization with improved scalability and derivative information for efficient design of nanophotonic structures Xavier Garcia Santiago Sven Burger C. Rockstuhl Philipp‐Immanuel Schneider 15 12 0 08 Jan 2021
Deep neural networks for the evaluation and design of photonic devices Jiaqi Jiang Ming-Keh Chen Jonathan A. Fan 21 393 0 30 Jun 2020
Using Gaussian process regression for efficient parameter reconstruction Philipp‐Immanuel Schneider M. Hammerschmidt L. Zschiedrich Sven Burger 6 14 0 28 Mar 2019
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces Johannes Kirschner Mojmír Mutný N. Hiller R. Ischebeck Andreas Krause 18 147 0 08 Feb 2019
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 89 271 0 24 Feb 2014