Physics-informed machine learning for the modeling and inverse design of microwave devices

This dissertation focuses on the development of physics-based machine learning models and their applications in the forward modeling and inverse design of microwave devices. There are two aspects of the research. First, we develop efficient surrogate modeling methods by incorporating physics knowledge into neural network models. Second, we propose a robust optimization technique utilizing the developed fast forward model. In the first part, we propose two neural network models which incorporate the second-order analytic extension of eigenvalues (AEE), and the second-order characteristic mode analysis (CMA). In these methods, the output nodes of neural networks are parameters of the physics models, instead of network parameters directly. This allows the models to learn a more reliable and generalizable mapping between the input and output. We introduce physics-based regularization to maximize information gain from data samples and advanced training strategies to further improve modeling efficiency. The proposed methods not only significantly reduce the cost for training data generation, but also demonstrate superior performance in terms of accuracy, superior data efficiency, and generalization capability. These models are differentiable, stackable, and fully compatible with system-level simulations. In the second part, a hybrid optimization framework combining genetic algorithms (GA) and gradient descent (grad-opt) is presented. In this approach, GA is used to start the optimization with random search and heuristics-based evolution. During the GA process, individual designs that satisfy a certain goodness criteria are seeded as the starting point for gradient-based updates, where an optimal solution can be reached quickly within only a few iterations. To facilitate the optimization process, we proposed the use of a neural network model that can speed up fitness evaluation in GA and gradient calculation in grad-opt. To further improve modeling efficiency and accelerate the design process when dealing with a large number of design variables, we adopt the divide-and-conquer strategy, which is fully compatible with the ML w/AEE model. We introduce a special neural network block called the fusion module to perform component cascading numerically, allowing the gradients to be passed from the objective function to design variables. We also propose a robust metric for global control, that involves the cost function value and its cumulative gradient. This control parameter allows us to achieve a good balance between efficiency and stability. We use two numerical examples to demonstrate the efficacy and strength of the proposed method. Hybrid optimization is shown to be much more efficient than standard GA and more robust than gradient-based methods.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Liu, Yanan

Contributors dc:contributor

• Jin, Jian-Ming
• Goddard, Lynford L.
• Feng, Milton
• Schutt-Ainé, José E.

• Machine Learning
• Neural Networks
• Microwave Devices
• Design Optimization

Statement dc:rights

• Copyright 2024 Yanan Liu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/125798

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F125798