AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization

• URL: http://arxiv.org/abs/2407.03356v1
• Date: Thu, 20 Jun 2024 19:00:33 GMT
• Title: AI Driven Laser Parameter Search: Inverse Design of Photonic Surfaces using Greedy Surrogate-based Optimization
• Authors: Luka Grbcic, Minok Park, Juliane Müller, Vassilia Zorba, Wibe Albert de Jong,
• Abstract summary: Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. We develop a surrogate-based optimization approach for designing such surfaces.
• Score: 0.7377466856726481
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photonic surfaces designed with specific optical characteristics are becoming increasingly important for use in in various energy harvesting and storage systems. , In this study, we develop a surrogate-based optimization approach for designing such surfaces. The surrogate-based optimization framework employs the Random Forest algorithm and uses a greedy, prediction-based exploration strategy to identify the laser fabrication parameters that minimize the discrepancy relative to a user-defined target optical characteristics. We demonstrate the approach on two synthetic benchmarks and two specific cases of photonic surface inverse design targets. It exhibits superior performance when compared to other optimization algorithms across all benchmarks. Additionally, we demonstrate a technique of inverse design warm starting for changed target optical characteristics which enhances the performance of the introduced approach.

Related papers

• H-Fac: Memory-Efficient Optimization with Factorized Hamiltonian Descent [11.01832755213396]
  We develop H-Fac, which incorporates a factorized approach to momentum and scaling parameters. Our algorithm demonstrates competitive performances on both ResNets and Vision Transformers. These optimization algorithms are designed to be both straightforward and adaptable, facilitating easy implementation in diverse settings.
  arXiv  Detail & Related papers  (2024-06-14T12:05:17Z)
• Hallmarks of Optimization Trajectories in Neural Networks: Directional Exploration and Redundancy [75.15685966213832]
  We analyze the rich directional structure of optimization trajectories represented by their pointwise parameters. We show that training only scalar batchnorm parameters some while into training matches the performance of training the entire network.
  arXiv  Detail & Related papers  (2024-03-12T07:32:47Z)
• Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies [0.8646443773218541]
  This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. Notably, this method is adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.
  arXiv  Detail & Related papers  (2023-12-06T18:20:46Z)
• Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofin And Annular Groove Phase Masks [40.11095094521714]
  Metasurfaces offer a flexible framework for the manipulation of light properties in the realm of thin film optics. This study aims to introduce a surrogate optimization framework for these devices. The framework is applied to develop two kinds of vortex phase masks (VPMs) tailored for application in astronomical high-contrast imaging.
  arXiv  Detail & Related papers  (2023-09-05T07:19:14Z)
• Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization [59.45414406974091]
  We introduce a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks. We study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimize solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs.
  arXiv  Detail & Related papers  (2023-05-31T12:07:50Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization [1.433758865948252]
  Physical simulation-based optimization is a common task in science and engineering. We develop a Bayesian optimization method leveraging tensor-based Gaussian process surrogates and trust region Bayesian optimization to effectively model the image outputs.
  arXiv  Detail & Related papers  (2021-11-29T19:29:35Z)
• Riggable 3D Face Reconstruction via In-Network Optimization [58.016067611038046]
  This paper presents a method for riggable 3D face reconstruction from monocular images. It jointly estimates a personalized face rig and per-image parameters including expressions, poses, and illuminations. Experiments demonstrate that our method achieves SOTA reconstruction accuracy, reasonable robustness and generalization ability.
  arXiv  Detail & Related papers  (2021-04-08T03:53:20Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Parameterized Reinforcement Learning for Optical System Optimization [0.0]
  A multi-layer optical system with designated optical characteristics is designed by several discrete and continuous parameters. Most methods merely determine the optimal thicknesses of the system's layers. We propose a method that considers the stacking of consecutive layers as parameterized actions in a Markov decision process.
  arXiv  Detail & Related papers  (2020-10-09T10:53:28Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.