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A manifold learning approach to data-driven computational elasticity and inelasticity. (English) Zbl 1390.74195
Summary: 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.

MSC:
74S60 Stochastic and other probabilistic methods applied to problems in solid mechanics
74A20 Theory of constitutive functions in solid mechanics
68T05 Learning and adaptive systems in artificial intelligence
62-07 Data analysis (statistics) (MSC2010)
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62P35 Applications of statistics to physics
Software:
DDDAS
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