首页|New Machine Learning Findings from University of Connecticut Discussed (Taming Connectedness In Machine-learning-based Topology Optimization With Connectivity Graphs)
New Machine Learning Findings from University of Connecticut Discussed (Taming Connectedness In Machine-learning-based Topology Optimization With Connectivity Graphs)
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NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news reporting out of Storrs, Connecticut, by NewsRx editors, research stated, “Despite the remarkable advancements in machine learning (ML) techniques for topology optimization, the predicted solutions often overlook the necessary structural connectivity required to meet the load-carrying demands of the resulting designs. Consequently, these predicted solutions exhibit subpar structural performance because disconnected components are unable to bear loads effectively and significantly compromise the manufacturability of the designs.In this paper, we propose an approach to enhance the topological accuracy of ML-based topology optimization methods by employing a predicted dual connectivity graph.” Financial supporters for this research include National Science Foundation (NSF), Office of Naval Research.
StorrsConnecticutUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Connecticut
NETL
NSTL
Elsevier
