Applications of Space Mapping Optimization Technology to Filter Design

One of the frontiers that remains in the optimization of large engineering systems is the successful application of optimization procedures in problems where direct optimization is not practical. The recent exploitation of surrogates in conjunction with “true” models, the development of artificial neural network approaches to device modeling and the implementation of space mapping are attempts to address this issue. Our original “Space Mapping” concept, first conceived in 1993, and the subsequent Aggressive Space Mapping approach to engineering design optimization will be discussed, along with new variations. Aggressive space mapping optimization closely follows the traditional experience and intuition of designers. It has been amply demonstrated as a very natural and flexible way of systematically optimizing microwave filters. Space mapping optimization intelligently links companion “coarse” and “fine” models of different complexities, e.g., full-wave electromagnetic simulations and empirical circuit-theory based simulations, to accelerate iterative design optimization of engineering structures. New trust region space mapping optimization algorithms will be mentioned. We briefly review the Expanded Space Mapping Design Framework (ESMDF) concept in which we allow preassigned parameters, not used in optimization, to change in some components of the coarse model. Other recent developments include the introduction of the object oriented SMX system to facilitate implementation of our algorithms in conjunction with certain commercial simulators. Extensive filter design examples complement the presentation.ITESO, A.C

Applications of Space Mapping Optimization Technology to Filter Design

One of the frontiers that remains in the optimization of large engineering systems is the successful application of optimization procedures in problems where direct optimization is not practical. The recent exploitation of surrogates in conjunction with “true” models, the development of artificial neural network approaches to device modeling and the implementation of space mapping are attempts to address this issue. Our original “Space Mapping” concept, first conceived in 1993, and the subsequent Aggressive Space Mapping approach to engineering design optimization will be discussed, along with new variations. Aggressive space mapping optimization closely follows the traditional experience and intuition of designers. It has been amply demonstrated as a very natural and flexible way of systematically optimizing microwave filters. Space mapping optimization intelligently links companion “coarse” and “fine” models of different complexities, e.g., full-wave electromagnetic simulations and empirical circuit-theory based simulations, to accelerate iterative design optimization of engineering structures. New trust region space mapping optimization algorithms will be mentioned. We briefly review the Expanded Space Mapping Design Framework (ESMDF) concept in which we allow preassigned parameters, not used in optimization, to change in some components of the coarse model. Other recent developments include the introduction of the object oriented SMX system to facilitate implementation of our algorithms in conjunction with certain commercial simulators. Extensive filter design examples complement the presentation.ITESO, A.C

A new AD/MDO approach to support product design

Nowadays, due to its critical role regarding product cost and performance, as well as time to market, product design is considered to be at the new frontiers for achieving competitive advantage. Therefore, to face todays rapidly changing business environments, it is extremely important to adopt a systematic approach to product design, in order to avoid errors and consequently achieve shorter time-to market performances. In this context, we will describe a new approach to support product design, which links Axiomatic Design (AD) and Multidisciplinary Design Optimization (MDO), applied in an integrated way at the conceptual design and the detailed design stages, respectively. Firstly, the conceptual design stage is undertaken by AD, which is used to map Functional Requirements (FRs) with the corresponding Design Parameters (DPs). Even though we try to guarantee the Independence of FRs, as established by Axiom 1 of AD, if some remaining coupled relations subsist that is not prohibitive. Afterwards, the detailed design is carried out by MDO, considered to be an appropriate methodology to design complex systems through an adequate exploitation of interacting phenomena. The proposed approach is applied to the design of metallic moulds for plastic parts injection, since the mould makers sector involves constant design and production of unrepeatable moulds, where uncoupled design solutions, mostly due to technological and time reasons, arent common (this sector is strongly influenced by customers who place enormous pressures on lead-time and cost reduction). This application points out the high potential of improvement that can be achieved through the simultaneous improvement of mould quality, reliability and time to market

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

Reinforcement Learning for Generative AI: A Survey

Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer vision. The major paradigm to train a generative model is maximum likelihood estimation, which pushes the learner to capture and approximate the target data distribution by decreasing the divergence between the model distribution and the target distribution. This formulation successfully establishes the objective of generative tasks, while it is incapable of satisfying all the requirements that a user might expect from a generative model. Reinforcement learning, serving as a competitive option to inject new training signals by creating new objectives that exploit novel signals, has demonstrated its power and flexibility to incorporate human inductive bias from multiple angles, such as adversarial learning, hand-designed rules and learned reward model to build a performant model. Thereby, reinforcement learning has become a trending research field and has stretched the limits of generative AI in both model design and application. It is reasonable to summarize and conclude advances in recent years with a comprehensive review. Although there are surveys in different application areas recently, this survey aims to shed light on a high-level review that spans a range of application areas. We provide a rigorous taxonomy in this area and make sufficient coverage on various models and applications. Notably, we also surveyed the fast-developing large language model area. We conclude this survey by showing the potential directions that might tackle the limit of current models and expand the frontiers for generative AI

Data Market Design through Deep Learning

The $\textit{data market design}$ problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price [Bergemann et al., 2018]. Each buyer has their own decision to make in a world environment, and their subjective expected value for the information associated with a particular experiment comes from the improvement in this decision and depends on their prior and value for different outcomes. In a setting with multiple buyers, a buyer's expected value for an experiment may also depend on the information sold to others [Bonatti et al., 2022]. We introduce the application of deep learning for the design of revenue-optimal data markets, looking to expand the frontiers of what can be understood and achieved. Relative to earlier work on deep learning for auction design [D\"utting et al., 2023], we must learn signaling schemes rather than allocation rules and handle $\textit{obedience constraints}$ $-$ these arising from modeling the downstream actions of buyers $-$ in addition to incentive constraints on bids. Our experiments demonstrate that this new deep learning framework can almost precisely replicate all known solutions from theory, expand to more complex settings, and be used to establish the optimality of new designs for data markets and make conjectures in regard to the structure of optimal designs

Enhancing the Engineering Curriculum: Defining Discovery Learning at Marquette University

This paper summarizes the results of our investigation into the feasibility of increasing the level of discovery learning in the College of Engineering (COE) at Marquette University. We review the education literature, document examples of discovery learning currently practiced in the COE and other schools, and propose a Marquette COE-specific definition of discovery learn-ing. Based on our assessment of the benefits, costs, and tradeoffs associated with increasing the level of discovery learning, we pre-sent several recommendations and identify resources required for implementation. These recommendations may be helpful in enhancing engineering education at other schools