Details
| Original language | English |
|---|---|
| Pages (from-to) | 2331-2338 |
| Number of pages | 8 |
| Journal | Building Simulation Conference Proceedings |
| Volume | 18 |
| Publication status | Published - 2023 |
| Event | 18th IBPSA Conference on Building Simulation, BS 2023 - Shanghai, China Duration: 4 Sept 2023 → 6 Sept 2023 |
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.
ASJC Scopus subject areas
- Engineering(all)
- Building and Construction
- Engineering(all)
- Architecture
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Science Applications
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In: Building Simulation Conference Proceedings, Vol. 18, 2023, p. 2331-2338.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Pathway toward prior knowledge-integrated machine learning in engineering
AU - Chen, Xia
AU - Geyer, Philipp
N1 - Funding Information: We gratefully acknowledge the German Research Foundation (DFG) support for funding the project under grant GE 1652/3-2 in the Researcher Unit FOR 2363 and under grant GE 1652/4-1 as a Heisenberg professorship.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85179507658&partnerID=8YFLogxK
U2 - 10.26868/25222708.2023.1481
DO - 10.26868/25222708.2023.1481
M3 - Conference article
AN - SCOPUS:85179507658
VL - 18
SP - 2331
EP - 2338
JO - Building Simulation Conference Proceedings
JF - Building Simulation Conference Proceedings
SN - 2522-2708
T2 - 18th IBPSA Conference on Building Simulation, BS 2023
Y2 - 4 September 2023 through 6 September 2023
ER -