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1. Verification & Validation of Computational Models associated with the Mechanics of Materials with the Mechanics of Materials.

   https://doi.org/www.dx.doi.org/10.7449/VandV_1.  

   The Minerals Metals & Materials Society, Inc (April 2019, Verification & Validation of Computational Models associated with the Mechanics of Materials with the Mechanics of Materials.)

   Predictive computational models associated with the mechanics of materials (MOM) offer great potential for enabling large reductions in the cost and time to develop new products and manufacturing procedures. Unfortunately, this potential is currently limited because very rarely are such models adequately and broadly proven to yield trustworthy, accurate, quantitative results for which the level of uncertainty has been quantified. In this regard, the need for rigorous verification and validation (V&V) of these models cannot be overestimated, yet is extremely lacking within the relevant MOM communities. There is thus a strong need to help these communities accelerate the widespread adoption and implementation of such V&V activities. In this vein, concise definitions of verification and validation have been provided by the American Society of Mechanical Engineers (ASME),1 and can be applied here as well: • Verification: The process of determining that a computational model accurately represents the underlying mathematical model and its solution • Validation: The process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model The overarching goal of this workshop and report is thus to help facilitate the widespread and rigorous adoption of V&V by both computational modelers and experimentalists in MOM-related communities. 

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2. Factors Mediating Learning and Application of Computational Modeling by Life Scientists

   https://doi.org/10.1109/FIE.2018.8659328  

   Madamanchi, Aasakiran; Cardella, Monica E.; Glazier, James A.; Umulis, David M. (October 2018, 2018 IEEE Frontiers in Education Conference (FIE))

   This Work-in-Progress paper in the Research Category uses a retrospective mixed-methods study to better understand the factors that mediate learning of computational modeling by life scientists. Key stakeholders, including leading scientists, universities and funding agencies, have promoted computational modeling to enable life sciences research and improve the translation of genetic and molecular biology high- throughput data into clinical results. Software platforms to facilitate computational modeling by biologists who lack advanced mathematical or programming skills have had some success, but none has achieved widespread use among life scientists. Because computational modeling is a core engineering skill of value to other STEM fields, it is critical for engineering and computer science educators to consider how we help students from across STEM disciplines learn computational modeling. Currently we lack sufficient research on how best to help life scientists learn computational modeling. To address this gap, in 2017, we observed a short-format summer course designed for life scientists to learn computational modeling. The course used a simulation environment designed to lower programming barriers. We used semi-structured interviews to understand students' experiences while taking the course and in applying computational modeling after the course. We conducted interviews with graduate students and post- doctoral researchers who had completed the course. We also interviewed students who took the course between 2010 and 2013. Among these past attendees, we selected equal numbers of interview subjects who had and had not successfully published journal articles that incorporated computational modeling. This Work-in-Progress paper applies social cognitive theory to analyze the motivations of life scientists who seek training in computational modeling and their attitudes towards computational modeling. Additionally, we identify important social and environmental variables that influence successful application of computational modeling after course completion. The findings from this study may therefore help us educate biomedical and biological engineering students more effectively. Although this study focuses on life scientists, its findings can inform engineering and computer science education more broadly. Insights from this study may be especially useful in aiding incoming engineering and computer science students who do not have advanced mathematical or programming skills and in preparing undergraduate engineering students for collaborative work with life scientists. 

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3. Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling

   Joshi, Shailendral; Bucsek, Ashleyl; Pagan, Darren C; Daly, Samantha; Ravindran, Suraj; Marian, Jaime; Bessa, Miguel A; Kalidindi, Surya R; Admal, Nikhil C; Reina, Celia; et al (March 2025, arXiv)

   The design of structural and functional materials for specialized applications is experiencing significant growth fueled by rapid advancements in materials synthesis, characterization, and manufacturing, as well as by sophisticated computational materials modeling frameworks that span a wide spectrum of length and time scales in the mesoscale between atomistic and homogenized continuum approaches. This is leading towards a systems-based design methodology that will replace traditional empirical approaches, embracing the principles of the Materials Genome Initiative. However, there are several gaps in this framework as it relates to advanced structural materials development: (1) limited availability and access to high-fidelity experimental and computational datasets, (2) lack of co-design of experiments and simulation aimed at computational model validation, (3) lack of on-demand access to verified and validated codes for simulation and for experimental analyses, and (4) limited opportunities for workforce training and educational outreach. These shortcomings stifle major innovations in structural materials design. This paper describes plans for a community-driven research initiative that addresses current gaps based on best-practice recommendations of leaders in mesoscale modeling, experimentation, and cyberinfrastructure obtained at an NSF-sponsored workshop dedicated to this topic and subsequent discussions. The proposal is to create a hub for "Mesoscale Experimentation and Simulation co-Operation (h-MESO)---that will (I) provide curation and sharing of models, data, and codes, (II) foster co-design of experiments for model validation with systematic uncertainty quantification, and (III) provide a platform for education and workforce development. h-MESO will engage experimental and computational experts in mesoscale mechanics and plasticity, along with mathematicians and computer scientists with expertise in algorithms, data science, machine learning, and large-scale cyberinfrastructure initiatives. 

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4. Recommend Me? Designing Fairness Metrics with Providers

   https://doi.org/10.1145/3630106.3659044  

   Smith, Jessie J; Satwani, Aishwarya; Burke, Robin; Fiesler, Casey (June 2024, ACM)

   Fairness metrics have become a useful tool to measure how fair or unfair a machine learning system may be for its stakeholders. In the context of recommender systems, previous research has explored how various stakeholders experience algorithmic fairness or unfairness, but it is also important to capture these experiences in the design of fairness metrics. Therefore, we conducted four focus groups with providers (those whose items, content, or profiles are being recommended) of two different domains: content creators and dating app users. We explored how our participants experience unfairness on their associated platforms, and worked with them to co-design fairness goals, definitions, and metrics that might capture these experiences. This work represents an important step towards designing fairness metrics with the stakeholders who will be impacted by their operationalizations. We analyze the efficacy and challenges of enacting these metrics in practice and explore how future work might benefit from this methodology. 

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5. Modeling Memorization: A Data Collection and Mathematical Modeling Experience

   https://doi.org/10.1080/10511970.2022.2068091  

   Tidwell, Will; Anhalt, Cynthia Oropesa; Cortez, Ricardo; Kohler, Brynja R. (January 2022, PRIMUS)

   This article presents a mathematical modeling activity for students related to the process of memorization in which students collect their own data to drive their model development, parameterization, and validation. Engaging in the data collection gives them insight to critique and evaluate various models. This task is a low-floor high ceiling problem that offers both a relatable context and a window to quantitative approaches in cognitive science. Experimental results of students’ participation in this activity are discussed. This article also includes pedagogical recommendations with a focus on fostering equitable teaching practices and a detailed analysis of the situation comprised of several mathematical approaches to model the memorization process that highlight the richness of the problem. Instructors can adapt and implement this modeling exploration for use in various undergraduate courses, from introductory to advanced, depending on the emphasis of the lesson. 

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