Physics-Informed Machine Learning and Hybrid Modelling
Author: Martin Paczona (Greenformance)
Conceptual-model-guided physics-inspired feature engineering (CPFE) a lightweight representation-level strategy that formalizes the incorporation of domain knowledge into machine-learning pipelines. Instead of embedding physics in the loss or model architecture, the proposed approach structures physical and operational knowledge at the representation level through traceable domain concepts. The method is demonstrated in a 3D-printer case study, where the resulting physics representation achieves better performance than a compact baseline and automatically extracted features using tsfresh.
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How to Cite: Paczona, M. (2026) “Conceptual-Model-Guided Physics-Inspired Feature Engineering: A 3D-Printer Case Study”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1). doi: https://doi.org/10.34749/3061-1466.2026.47