Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
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Abstract (arXiv)
Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To design and understand these nanophotonic structures, electrodynamic simulations are essential. These simulations enable us to model electromagnetic fields over time and calculate optical properties. In this work, we introduce frameworks and benchmarks to evaluate nanophotonic structures in the context of parametric structure design problems. The benchmarks are instrumental in assessing the performance of optimization algorithms and identifying an optimal structure based on target optical properties. Moreover, we explore the impact of varying grid sizes in electrodynamic simulations, shedding light on how evaluation fidelity can be strategically leveraged in enhancing structure designs.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 79 references that are in the map. First posted on arXiv 29 Oct 2023.
Builds on
20 of 79 references.
References · 20
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
- 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 416
- 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 344
- 2018GuacaMol: Benchmarking Models for De Novo Molecular Designcited by 297
- 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 148
- 2017Multi-fidelity Bayesian Optimisation with Continuous Approximationscited by 121
- 2021HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPOcited by 112
- 2020NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Sizecited by 98
- 2015Predictive Entropy Search for Multi-objective Bayesian Optimizationcited by 88
- 2020Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraintscited by 81
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- 2021YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimizationcited by 62
- 2019Tabular Benchmarks for Joint Architecture and Hyperparameter Optimizationcited by 58
- 2021LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lassocited by 41
- 2022Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systemscited by 23
- 2020Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approachcited by 22
- 2021Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structurecited by 15
- 2022Inverse design of photonic devices with strict foundry fabrication constraintscited by 11
- 2018Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solverscited by 9
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