2009 | OriginalPaper | Buchkapitel
Pareto-Based Multi-output Metamodeling with Active Learning
verfasst von : Dirk Gorissen, Ivo Couckuyt, Eric Laermans, Tom Dhaene
Erschienen in: Engineering Applications of Neural Networks
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
When dealing with computationally expensive simulation codes or process measurement data, global surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, and splines. In addition, the cost of each simulation mandates the use of active learning strategies where data points (simulations) are selected intelligently and incrementally. When applying surrogate models to multi-output systems, the hyperparameter optimization problem is typically formulated in a single objective way. The different response outputs are modeled separately by independent models. Instead, a multi-objective approach would benefit the domain expert by giving information about output correlation, facilitate the generation of diverse ensembles, and enable automatic model type selection for each output on the fly. This paper outlines a multi-objective approach to surrogate model generation including its application to two problems.