Surrogate models are often employed to speed up engineering design optimization; however, they typically require that all training data conform to the same parametrization (e.g. design variables), limiting design freedom and prohibiting the reuse of historical data. In response, this paper proposes Graph-based Surrogate Models (GSMs) for space frame structures. The GSM can accurately predict displacement fields from static loads given the structure's geometry as input, enabling training across multiple parametrizations. GSMs build upon recent advancements in geometric deep learning which have led to the ability to learn on undirected graphs: a natural representation for space frames. To further promote flexible surrogate models, the paper explores transfer learning within the context of engineering design, and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads and applications, resulting in more flexible and data-efficient surrogate models for space frame structures.

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This paper introduces the Simulated Jet Engine Bracket Dataset (SimJEB) [WBM21]: a new, public collection of crowdsourced mechanical brackets and accompanying structural simulations. SimJEB is applicable to a wide range of geometry processing tasks; the complexity of the shapes in SimJEB offer a challenge to automated geometry cleaning and meshing, while categorical labels and structural simulations facilitate classification and regression (i.e. engineering surrogate modeling). In contrast to existing shape collections, SimJEB's models are all designed for the same engineering function and thus have consistent structural loads and support conditions. On the other hand, SimJEB models are more complex, diverse, and realistic than the synthetically generated datasets commonly used in parametric surrogate model evaluation. The designs in SimJEB were derived from submissions to the GrabCAD Jet Engine Bracket Challenge: an open engineering design competition with over 700 hand‐designed CAD entries from 320 designers representing 56 countries. Each model has been cleaned, categorized, meshed, and simulated with finite element analysis according to the original competition specifications. The result is a collection of 381 diverse, high‐quality and application‐focused designs for advancing geometric deep learning, engineering surrogate modeling, automated cleaning and related geometry processing tasks.

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Sunny Jain has spent his career as a drummer and composer, bridging the gap between Eastern and Western music. As the son of Punjabi immigrant parents, born and raised in Rochester, New York, Jain played around on tablas, sang the traditional bhajans, and, by age 12, had gravitated toward the essential American musical genre, jazz. In the early aughts Jain served as a U.S. State Department Ambassador of Jazz, toured the world with Pakistani Sufi Rock band Junoon, and released several albums with the Sunny Jain Collective. In 2008, Jain founded Red Baraat, an eight-piece band inspired by the brass-heavy, raucously joyful spirit of a North Indian wedding procession-a baraat-fused with funk, jazz, rock, D.C. go-go, and, to hear Jain tell it, whatever sound inspires. Before a recording session for Red Baraat's fourth album, Jain spoke about the band, the social function of their high-energy shows, South Asian pop-cultural visibility, and serving as a musical embodiment of pluralism in a time of heightened nationalism.