Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization

Abstract

The sampling of the training data is a bottleneck in the development of artificial intelligence (AI) models due to the processing of huge amounts of data or to the difficulty of access to the data in industrial practices. Active learning (AL) approaches are useful in such a context since they maximize the performance of the trained model while minimizing the number of training samples. Such smart sampling methodologies iteratively sample the points that should be labeled and added to the training set based on their informativeness and pertinence. To judge the relevance of a data instance, query rules are defined. In this paper, we propose an AL methodology based on a physics-based query rule. Given some industrial objectives from the physical process where the AI model is implied in, the physics-based AL approach iteratively converges to the data instances fulfilling those objectives while sampling training points. Therefore, the trained surrogate model is accurate where the potentially interesting data instances from the industrial point of view are, while coarse everywhere else where the data instances are of no interest in the industrial context studied.