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Geodesic Convolutional Neural Network for 3D Deep-Learning based Surrogate Modeling and Optimization

Baqué, Pierre and Allard, Théophile and Baset, Selena and Vont Tschammer, Thomas and Zampieri, Luca and Fua, Pascal Geodesic Convolutional Neural Network for 3D Deep-Learning based Surrogate Modeling and Optimization. (2020) In: 1st International Conference on Cognitive Aircraft Systems - ICCAS 2020, 18 March 2020 - 19 March 2020 (Toulouse, France). (Unpublished)

(Document in English)

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The role of numerical simulation in product development has shifted from being a validation tool of mature designs into a means of exploration of product design space. Yet, the time required to run a simulation is, most of the time, a bottleneck in the engineer’s optimisation loop and for larger design spaces it can result in automated shape optimization being simply intractable. This needs to be addressed on the way to better simulation-driven design. Surrogate models are used in CFD simulations, and other computationally intensive simulations, as a cheaper data-driven substitute for the full-fledged numerical simulator. Most of the existing surrogate modeling approaches rely on Gaussian Process regressors (Kriging) and are thus limited to predicting the performance of shapes with a fixed low-dimensional parameterization. On top of that, kriging methods are meant for predicting global scalar values but they are not capable of predicting fields (e.g. velocity or pressure values at every point of the shape).

Item Type:Invited Conference
Audience (conference):International conference without published proceedings
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Deposited On:09 May 2021 13:35

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