May 14, 2024, 4:43 a.m. | Philipp Geyer, Manav Mahan Singh, Xia Chen

cs.LG updates on

arXiv:2108.13836v5 Announce Type: replace
Abstract: Data-driven models created by machine learning, gain in importance in all fields of design and engineering. They, have high potential to assist decision-makers in creating novel, artefacts with better performance and sustainability. However,, limited generalization and the black-box nature of these models, lead to limited explainability and reusability. To overcome this, situation, we propose a component-based approach to create, partial component models by machine learning (ML). This, component-based approach aligns deep learning with systems, engineering …

abstract arxiv box cs.lg data data-driven decision deep learning design engineering engineering design explainable ai fields however importance machine machine learning makers nature novel performance replace sustainability systems type

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