Taming complexity in computational electromagnetism: a model order reduction approach

控制计算电磁学的复杂性：模型降阶方法

• 批准号: RGPIN-2019-05060
• 负责人: Triverio, Piero
• 金额: $ 2.4万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751664
• 关键词: Taming; complexity; computational; electromagnetism; model

Electromagnetic phenomena play a critical role inside the integrated circuits (ICs) and antennas that empower many electronic systems around us, including smartphones, computers, and data centers. Behind the scenes, electromagnetism also plays a crucial role in emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). It is estimated that, by 2020, 20 billion IoT devices will be connected to the Internet. Electromagnetic waves, and significant advancements in antenna technology, will be the only way to connect the myriad of future IoT devices to the Internet, due to the prohibitive cost of terrestrial infrastructure. Metasurfaces are a new type of antennas which is ideally suited to respond to soaring IoT needs. Unfortunately, metasurfaces are extremely complex. Since no existing electromagnetic simulator can handle their complexity, engineers are currently missing one of the most critical tools for metasurface design. Similar challenges are faced by microelectronic companies such as AMD, which extensively rely on electromagnetic simulators to design new ICs, including the hardware accelerators of neural networks that are increasingly needed by advanced AI applications. The significant gap between what electromagnetic solvers can do and what designers need is a major issue for both industry and academia. The proposed research aims to fill this gap by laying down the theoretical and algorithmic foundations of an electromagnetic solver with superior scalability. We propose to investigate an innovative approach based on the concept of model order reduction. While model order reduction is well established for circuit simulation, it has received limited attention in computational electromagnetism, despite a clear need. We propose to develop the first systematic techniques to create compact, reduced-complexity models of the electromagnetic behavior of complex objects, such as the unit cell of a metasurface. The proposed models will be compatible with mainstream simulators based on the method of moments, allowing designers to analyze very large layouts in reasonable time. Leveraging the proposed developments and parallel computing, we will create an electromagnetic solver with unprecedented scalability, capable of handling an entire metasurface antenna. These developments will greatly facilitate the design of new metasurface antennas and ICs, including those required by upcoming IoT and AI applications. The proposed developments will be immediately translated to real design workflows, in collaboration with leading companies (AMD, Thales) and scientists (Profs. Eleftheriades and Hum). The proposed research will involve nine students who will receive comprehensive training of the highest quality, with access to world-class industrial partners, facilities and forefront technologies. Overall, this program is expected to result in nine graduates with a unique skillset, in high demand by industry and academia.

电磁现象在综合电路（ICS）和天线中起着至关重要的作用，这些天线赋予我们周围许多电子系统的能力，包括智能手机，计算机和数据中心。在幕后，电磁主义在新兴技术（例如物联网（IoT）和人工智能（AI））中也起着至关重要的作用。据估计，到2020年，将有200亿个IoT设备连接到Internet。由于地面基础设施的高昂成本，电磁波以及天线技术的重大进步将是将未来的IoT设备连接到Internet的唯一方法。 Metasurfaces是一种新型天线，非常适合响应飙升的物联网需求。不幸的是，元信息非常复杂。由于现有的电磁模拟器无法处理其复杂性，因此工程师目前缺少元图设计最关键的工具之一。像AMD这样的微电子公司面临类似的挑战，这些公司广泛依靠电磁模拟器来设计新的IC，包括高级AI应用程序越来越需要的神经网络的硬件加速器。 电磁求解器可以做什么，而设计人员需要什么是行业和学术界的主要问题。拟议的研究旨在通过降低具有较高可扩展性的电磁求解器的理论和算法基础来填补这一空白。我们建议根据减少模型顺序的概念研究一种创新方法。虽然降低模型顺序用于电路仿真，但尽管需要明确的需求，但它仍受到计算电磁作用的有限关注。我们建议开发第一个系统的技术，以创建复杂物体的电磁行为的紧凑，减少复杂模型，例如元曲面的单位单元格。提出的模型将基于矩的方法与主流模拟器兼容，从而使设计人员可以在合理的时间内分析非常大的布局。利用所提出的开发和并行计算，我们将创建一个具有前所未有的可扩展性的电磁求解器，能够处理整个跨表面天线。这些发展将极大地促进新的元面天线和IC的设计，包括即将到来的IoT和AI应用程序所需的IC。拟议的发展将立即与领先公司（AMD，Thales）和科学家（Eleftheriades和Hum教授）合作，将其转化为真正的设计工作流程。拟议的研究将涉及九名学生，他们将获得最高质量的全面培训，并获得世界一流的工业伙伴，设施和前沿技术。总体而言，该计划有望导致九名具有独特技能的毕业生，该计划的需求量很高。