More Like this

1. In-Processing Modeling Techniques for Machine Learning Fairness: A Survey

   https://doi.org/10.1145/3551390  

   Wan, Mingyang; Zha, Daochen; Liu, Ninghao; Zou, Na (January 2022, ACM Transactions on Knowledge Discovery from Data)

   Machine learning models are becoming pervasive in high-stakes applications. Despite their clear benefits in terms of performance, the models could show discrimination against minority groups and result in fairness issues in a decision-making process, leading to severe negative impacts on the individuals and the society. In recent years, various techniques have been developed to mitigate the unfairness for machine learning models. Among them, in-processing methods have drawn increasing attention from the community, where fairness is directly taken into consideration during model design to induce intrinsically fair models and fundamentally mitigate fairness issues in outputs and representations. In this survey, we review the current progress of in-processing fairness mitigation techniques. Based on where the fairness is achieved in the model, we categorize them into explicit and implicit methods, where the former directly incorporates fairness metrics in training objectives, and the latter focuses on refining latent representation learning. Finally, we conclude the survey with a discussion of the research challenges in this community to motivate future exploration. 

   more » « less
2. Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and Prospect

   https://doi.org/10.1145/3661308  

   Wu, Nan; Li, Yingjie; Yang, Hang; Chen, Hanqiu; Dai, Steve; Hao, Cong; Yu, Cunxi; Xie, Yuan (July 2024, ACM Transactions on Design Automation of Electronic Systems)

   With the ever-increasing hardware design complexity comes the realization that efforts required for hardware verification increase at an even faster rate. Driven by the push from the desired verification productivity boost and the pull from leap-ahead capabilities of machine learning (ML), recent years have witnessed the emergence of exploiting ML-based techniques to improve the efficiency of hardware verification. In this article, we present a panoramic view of how ML-based techniques are embraced in hardware design verification, from formal verification to simulation-based verification, from academia to industry, and from current progress to future prospects. We envision that the adoption of ML-based techniques will pave the road for more scalable, more intelligent, and more productive hardware verification. 

   more » « less
3. Advanced Modeling and Optimization Strategies for Process Synthesis

   https://doi.org/10.1146/annurev-chembioeng-100522-112139  

   Pistikopoulos, Efstratios N; Tian, Yuhe (July 2024, Annual Review of Chemical and Biomolecular Engineering)

   This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation–based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning–and quantum computing–assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies. 

   more » « less
4. Power Converter and Discrete Device Optimization Utilizing Discrete Time State-Space Modeling

   https://doi.org/10.1109/COMPEL52896.2023.10221030  

   Baxter, Jared A; Costinett, Daniel J (June 2023, 2023 IEEE 24th Workshop on Control and Modeling for Power Electronics (COMPEL))

   Broad-scale modeling and optimization play a vital role in the design of advanced power converters. Optimization is normally implemented via brute force iterations of design variables or utilizing metaheuristic techniques which are time consuming for a wide range of potential topologies, device implementations, and operating points. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques across multiple converter parameters. In this work, we investigate methods to incorporate rapid gradient-based optimization techniques to leverage discrete time state-space modeling and showcase the approach in the power converter design process. The method is validated on a 48-to-1V converter designed using the proposed techniques. 

   more » « less
5. Systems Approaches to Materials Design: Past, Present, and Future

   https://doi.org/10.1146/annurev-matsci-070218-125955  

   Arróyave, Raymundo; McDowell, David L. (July 2019, Annual Review of Materials Research)

   There is increasing awareness of the imperative to accelerate materials discovery, design, development, and deployment. Materials design is essentially a goal-oriented activity that views the material as a complex system of interacting subsystems with models and experiments at multiple scales of materials structure hierarchy. The goal of materials design is effectively to invert quantitative relationships between process path, structure, and materials properties or responses to identify feasible materials. We first briefly discuss challenges in framing process-structure-property relationships for materials and the critical role of quantifying uncertainty and tracking its propagation through analysis and design. A case study exploiting inductive design of ultrahigh-performance concrete is briefly presented. We focus on important recent directions and key scientific challenges regarding the highly collaborative intersections of materials design with systems engineering, uncertainty quantification and management, optimization, and materials data science and informatics, which are essential to fueling continued progress in systems-based materials design. 

   more » « less