A data driven framework for evolutionary problems in solid mechanics
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Date
2021-11
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Abstract
Data driven schemes introduced a new perspective in
elasticity: While certain physical principles are regarded as invariable,
material models for the relation between strain and stress are replaced
by data clouds of admissible pairs of these variables. A data driven
approach is of particular interest for plasticity problems, since the
material modelling is even more unclear in this field. Unfortunately,
so far, data driven approaches to evolutionary problems are much
less understood. We try to contribute in this area and propose
an evolutionary data driven scheme. We presenta first analysis of
the scheme regarding existence and data convergence. Encouraging
numerical tests are also included.
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Keywords
plasticity, data driven, history surrogate, neural network