Pathway toward prior knowledge-integrated machine learning in engineering
It tackles the problem of bridging first-principles and data-driven models for engineers, but appears incremental as it builds on existing debates without claiming major breakthroughs.
The study addresses the challenge of integrating domain knowledge into data-driven machine learning processes in engineering, proposing a three-tier knowledge-integrated paradigm to balance holistic and reductionist perspectives.
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.