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Found 7 papers, 2 papers with code
Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework
In this study, we propose to adopt a novel framework, Knowledge-integrated Machine Learning, for advancing Proton Exchange Membrane Water Electrolysis (PEMWE) development.
Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry
The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models.
Pathway toward prior knowledge-integrated machine learning in engineering
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.
Utilizing Domain Knowledge: Robust Machine Learning for Building Energy Prediction with Small, Inconsistent Datasets
In this study, component-based machine learning (CBML) as the knowledge-encoded data-driven method is examined in the context of energy-efficient building engineering.
A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation
The framework is integrated with a feedforward loop that embedded a dynamic building environment with leading and lagging system information involved: The simulation combined with system characteristic information is imported to the ML predictive algorithms.
Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX
This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals.
Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning
The, approach adapts the model structure to engineering methods of, systems engineering and to domain knowledge.
