Pathway toward prior knowledge-integrated machine learning in
  engineering
Xia Chen Philipp Geyer
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.