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- W4384613813 abstract "Despite the digitalization trend and data volume surge, first-principles models (also known as logic-driven, physics-based, rule-based, or knowledge-based models) and data-driven approaches have existed in parallel, mirroring the ongoing AI debate on symbolism versus connectionism. Research for process development to integrate both sides to transfer and utilize domain knowledge in the data-driven process is rare. This study emphasizes efforts and prevailing trends to integrate multidisciplinary domain professions into machine acknowledgeable, data-driven processes in a two-fold organization: examining information uncertainty sources in knowledge representation and exploring knowledge decomposition with a three-tier knowledge-integrated machine learning paradigm. This approach balances holist and reductionist perspectives in the engineering domain." @default.
- W4384613813 created "2023-07-18" @default.
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- W4384613813 date "2023-07-10" @default.
- W4384613813 modified "2023-09-25" @default.
- W4384613813 title "Pathway toward prior knowledge-integrated machine learning in engineering" @default.
- W4384613813 doi "https://doi.org/10.48550/arxiv.2307.06950" @default.
- W4384613813 hasPublicationYear "2023" @default.
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