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The work addresses the optimization of specimen geometry in the context of parameter identification from full-field measurements. For this purpose, we propose to use topology optimization tools in order to maximize the sensitivity of the measured displacement field to sought parameters, under volume fraction constraints and without any a priori information on the specimen shape. The associated cost function is conveniently defined from sensitivity fields which are directly available from the identification procedure. In addition, further analyzes are conducted in order to fully set the theoretical and numerical aspects of the methodology, and enhance both stability and convergence properties. Numerical experiments illustrate the interest of the proposed approach, while its validation is performed from a real identification test using a specimen with optimized topology. [ABSTRACT FROM AUTHOR]

Highlights: [•] Construction of a generic transition between nonlocal and local discrete models. [•] Approximation of the energy both free of ghost forces and energy preserving. [•] Performances analyzed both for statics and dynamics problems. [•] Highlights on the influence of energy distribution in dynamics. [ABSTRACT FROM AUTHOR]