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In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time‐varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generatein situthat are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time‐varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem — learning a function‐space neural network to reproduce flow map samples under a fixed integration scheme — leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map.

 

The current study utilized the intersectionality theory to analyze microaggressions towards engineering undergraduate underrepresented gender and racial minority students. In this study, participants were sampled from intersecting identity groups (Asian female, Asian male, Black female, Black male, Hispanic female, Hispanic male, White female) at two institutional settings: 1) a Historically Black College/University (HBCU) and 2) a Predominantly White Institution (PWI). The study’s analysis examined microaggressions in the context of undergraduate engineering programs at both sites, an HBCU and a PWI. The results suggested that a higher frequency of microaggressions took place at the PWI than the HBCU. The most frequently identified microaggressions included disjointed race and gender dialogue, hidden language, projected stereotypes, an ascription of intelligence, silence, and marginalization. The paper aims to increase awareness of the prevalence and varying types of microaggressions experienced between the sites. These results may influence policies and educational practices to meet the needs of underrepresented minority students in engineering. This material is based upon work supported by the National Science Foundation under Grant No. (1828172 and 1828559). “Collaborative Research: An Intersectional Perspective to Studying Microaggressions in Engineering Programs”. 

We present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neural network maps a point in the domain to an output scalar value. By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalar fields, thus framing compression as a type of function approximation. Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state‐of‐the‐art volume compression approaches. The conceptual simplicity of our approach enables a number of benefits, such as support for time‐varying scalar fields, optimizing to preserve spatial gradients, and random‐access field evaluation. We study the impact of network design choices on compression performance, highlighting how simple network architectures are effective for a broad range of volumes.

 

Microaggressions are widespread in engineering but have received limited attention from engineering education scholars. This research presents the current state of literature on microaggressions and emphasizes the need to adopt an intersectionality perspective to studying mciroaggressions. The research presents a review of the literature including the (1) study context, (2) study methods, (3) study objectives, (4) microaggressions outcomes and (5) microaggressions types using data from 45 journal articles. Data analysis included coding of the journal articles to identify major themes representing different forms of microaggressions. The current results show that the research studying microaggressions using an intersectional lens is limited. This research contributes to improved understanding regarding microaggressions by identifying the gaps within existing literature on microaggressions. Practically, this research increases the visibility of subtle negative behaviors that engineering minority groups experience and their importance for students’ success and persistence.