Designing artificial neural networks for band structures computations in photonic crystals

We modeled Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks (ANNs) for computing band structures (BSTs) and photonic band gaps (PBGs) of 2D and 3D photonic crystals (PhCs). We aim at providing fast ANN models which might boost the computations of BDs and PBGs regarding electromagnetic solvers. The case studies considered 2D and 3D PhCs with different lattices, geometries, and materials. Datasets for ANN training were built by varying the geometric shapes' dimensions and the dielectric constants of the case-study PhCs. We demonstrate simple and fast-training ANNs capable of providing accurate BSTs and PGBs through speedy computations10912SPIE OPTO - Physics and Simulation of Optoelectronic Devices XXVI

Computer-inspired Quantum Experiments

The design of new devices and experiments in science and engineering has historically relied on the intuitions of human experts. This credo, however, has changed. In many disciplines, computer-inspired design processes, also known as inverse-design, have augmented the capability of scientists. Here we visit different fields of physics in which computer-inspired designs are applied. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we draw our attention specifically on quantum physics. In the quest for designing new quantum experiments, we face two challenges: First, quantum phenomena are unintuitive. Second, the number of possible configurations of quantum experiments explodes combinatorially. To overcome these challenges, physicists began to use algorithms for computer-designed quantum experiments. We focus on the most mature and \textit{practical} approaches that scientists used to find new complex quantum experiments, which experimentalists subsequently have realized in the laboratories. The underlying idea is a highly-efficient topological search, which allows for scientific interpretability. In that way, some of the computer-designs have led to the discovery of new scientific concepts and ideas -- demonstrating how computer algorithm can genuinely contribute to science by providing unexpected inspirations. We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future. Finally, we discuss what we can learn from the different approaches in the fields of physics, and raise several fascinating possibilities for future research.Comment: Comments and suggestions for additional references are welcome

Uma abordagem baseada em redes neurais artificiais para computação de propriedades ópticas de cristais fotônicos

Orientadores: Hugo Enrique Hernández-Figueroa, Gilliard Nardel Malheiros SilveiraTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Esta tese aborda o emprego de processos baseados em redes neurais artificiais para computação de relações de dispersão e banda fotônica proibida de cristais fotônicos. A proposta objetiva prover um modelo de computação alternativo capaz de calcular rapidamente estas propriedades ópticas em relação às simulações eletromagnéticas convencionais. O modelo é baseado nas redes neurais artificiais Perceptron de Múltiplas Camadas e Máquinas de Aprendizado Extremo, que são projetadas para processarem dados geométricos e de materiais de cristais fotônicos e assim predizerem estas propriedades ópticas. Uma arquitetura simples de rede neural é proposta para permitir processos rápidos de treinamento. O modelo é testado em uma variedade de cristais fotônicos bi- and tri-dimensionais com arranjos, geometrias, e materiais diferentes, e sua capacidade de predição e desempenho de computação são avaliados em relação a um simulador eletromagnético bem estabelecido na comunidade de fotônicaAbstract: This thesis addresses the employment of Artificial Neural Network-based processes for computing dispersion relations and photonic bandgaps of photonic crystals. The proposal aims to provide an alternative computing model able to fastly calculate these optical properties regarding conventional electromagnetic simulations. The model is based on Multilayer Perceptron and Extreme Learning Machine Artificial Neural Networks, which are designed to process the geometric and material data of photonic crystals in order to predict such optical properties. A simple neural-network architecture is proposed for allowing fast training processes. The model is tested on a variety of bi- and tri-dimensional photonic crystals with different lattices, geometries, and materials, and its predicting capability and computing performance are evaluated in regard to a well-established electromagnetic simulator in photonic communityDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia ElétricaCAPE

A systematic approach for integrated product, materials, and design-process design

Designers are challenged to manage customer, technology, and socio-economic uncertainty causing dynamic, unquenchable demands on limited resources. In this context, increased concept flexibility, referring to a designer s ability to generate concepts, is crucial. Concept flexibility can be significantly increased through the integrated design of product and material concepts. Hence, the challenge is to leverage knowledge of material structure-property relations that significantly affect system concepts for function-based, systematic design of product and materials concepts in an integrated fashion. However, having selected an integrated product and material system concept, managing complexity in embodiment design-processes is important. Facing a complex network of decisions and evolving analysis models a designer needs the flexibility to systematically generate and evaluate embodiment design-process alternatives. In order to address these challenges and respond to the primary research question of how to increase a designer s concept and design-process flexibility to enhance product creation in the conceptual and early embodiment design phases, the primary hypothesis in this dissertation is embodied as a systematic approach for integrated product, materials and design-process design. The systematic approach consists of two components i) a function-based, systematic approach to the integrated design of product and material concepts from a systems perspective, and ii) a systematic strategy to design-process generation and selection based on a decision-centric perspective and a value-of-information-based Process Performance Indicator. The systematic approach is validated using the validation-square approach that consists of theoretical and empirical validation. Empirical validation of the framework is carried out using various examples including: i) design of a reactive material containment system, and ii) design of an optoelectronic communication system.Ph.D.Committee Chair: Allen, Janet K.; Committee Member: Aidun, Cyrus K.; Committee Member: Klein, Benjamin; Committee Member: McDowell, David L.; Committee Member: Mistree, Farrokh; Committee Member: Yoder, Douglas P