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Metamodeling and Machine Learning

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Abstract

A metamodel, or surrogate model, is a model of a model. Metamodeling refers to a process of generating such metamodels which is based on analysis, construction and development of the frames, rules, constraints, models and theories applicable and useful for modeling a predefined class of problems. On the other hand, machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. This chapter provides a review on different design of experiment techniques, as well as various machine learning algorithms.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Colorado, Boulder, CO, USA

    Victor E. Saouma & M. Amin Hariri-Ardebili

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  1. Victor E. Saouma
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  2. M. Amin Hariri-Ardebili
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Saouma, V.E., Hariri-Ardebili, M.A. (2021). Metamodeling and Machine Learning. In: Aging, Shaking, and Cracking of Infrastructures. Springer, Cham. https://doi.org/10.1007/978-3-030-57434-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-57434-5_20

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  • Print ISBN: 978-3-030-57433-8

  • Online ISBN: 978-3-030-57434-5

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