Pathway toward prior knowledge-integrated machine learning in engineering
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 54 references that are in the map. First posted on arXiv 10 Jul 2023.
Builds on
14 of 54 references.
References · 14
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2 more
- 2021Explainable AI for Engineering Design: A Unified Approach of Systems Engineering and Component- Based Deep Learning Demonstrated by Energy- Efficient Building Designcited by 3
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Citing papers per year: 2024: 2.
2 papers.