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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity
Abstract Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,...), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. Even if one can be confident on the first type of equations, the second one contains modeling errors. Moreover, this second type of equations remains too particular and often fails in describing new experimental results. The vast majority of existing models lack of generality, and therefore must be constantly adapted or enriched to describe new experimental findings. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ axiomatic, universal laws while minimizing the need of explicit, often phenomenological, models. This technique is based on the use of manifold learning methodologies, that allow to extract the relevant information from large experimental datasets.
A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity
Abstract Standard simulation in classical mechanics is based on the use of two very different types of equations. The first one, of axiomatic character, is related to balance laws (momentum, mass, energy,...), whereas the second one consists of models that scientists have extracted from collected, natural or synthetic data. Even if one can be confident on the first type of equations, the second one contains modeling errors. Moreover, this second type of equations remains too particular and often fails in describing new experimental results. The vast majority of existing models lack of generality, and therefore must be constantly adapted or enriched to describe new experimental findings. In this work we propose a new method, able to directly link data to computers in order to perform numerical simulations. These simulations will employ axiomatic, universal laws while minimizing the need of explicit, often phenomenological, models. This technique is based on the use of manifold learning methodologies, that allow to extract the relevant information from large experimental datasets.
A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity
Ibañez, Rubén (Autor:in) / Abisset-Chavanne, Emmanuelle (Autor:in) / Aguado, Jose Vicente (Autor:in) / Gonzalez, David (Autor:in) / Cueto, Elias (Autor:in) / Chinesta, Francisco (Autor:in)
2016
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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