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Recent Advances in Design Optimization under Uncertainty

Subhayan De, University of Colorado

Alireza Doostan, University of Colorado

Kurt Maute, University of Colorado

The presence of uncertainty in engineering systems is ubiquitous. For example, there are intrinsic variabilities associated with microscale material properties, manufacturing processes, or operating conditions of a structure. As such, achieving robust designs that are least insensitive to such variability or satisfy certain failure probability is of practical importance.  Standard design optimization techniques relying on statistical moments or reliability estimates as the objective or constraints may lead to prohibitive computational cost. In recent years, methods such as reduced-order modeling, multi-level and multi-fidelity formulations, stochastic gradients, and so on have shown promise to reduce the overall design cost. 


The primary focus of this mini-symposium is on recent developments in computational techniques for shape, topology, and PDE-constraint optimization of engineering systems in the presence of uncertainty at single and multiple scales. This includes novel optimization algorithms and methods, numerical and computational challenges, as well as applications to engineering problems.