Optimising test sequences for robust material identification using a data assimilation approach
Résumé
This study investigates the optimisation of test sequences for robust material identification. Traditional approaches for constitutive behaviour characterisation usually consist in a sequence of independent identification problems, each one considering one single experiment and a small subset of parameters. Conversely, data assimilation approaches consider all measurements stemming from all experiments at once. This work demonstrates the potential of data assimilation to improve multiple parameters identification by optimising the experimental set-up of the tests sequence.
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