工程领域中融合先验知识的机器学习之路
Pathway toward prior knowledge-integrated machine learning in engineering
AI总结：
针对第一性原理模型与数据驱动方法长期并行的问题，本文提出三层知识融合机器学习范式，通过分析知识表示不确定性与知识分解来整合领域先验知识。
摘要：
尽管数字化趋势不断发展且数据量激增，第一性原理模型（也称为逻辑驱动、基于物理、基于规则或基于知识的模型）与数据驱动方法却一直并行存在，这反映了人工智能领域中符号主义与联结主义之间持续的争论。目前，关于如何整合两者以在数据驱动过程中迁移和利用领域知识的流程开发研究仍然较少。本研究强调将多学科领域专业知识整合到机器可识别的数据驱动过程中的相关努力与主流趋势，并以两部分组织：考察知识表示中的信息不确定性来源，以及通过三层知识融合机器学习范式探索知识分解。该方法在工程领域平衡了整体论与还原论视角。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
