Knowledge-Guided Additive Modeling For Supervised Regression

Several hybrid approaches, incorporating prior domain knowledge within machine learning (ML), have recently been introduced to improve generalization and robustness. However, such hybrid methods were mostly tested on dynamical systems, with only limited study of the influence of each model component on global performance and parameter identification. In this work, we assess the performance of hybrid modeling on standard regression problems: we compare, on synthetic problems, several approaches for training such hybrid models, focusing on model-agnostic methods that additively combine a parametric physical term with an ML term. We also introduce a new hybrid approach based on partial dependence functions. Experiments are carried out with different types of ML models, including tree-based models and neural networks.