Computational methods for modeling and design of complex engineering systems under uncertainty

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Computation-based design is starting to become the norm in many engineering sectors and high-fidelity models are now increasingly used to support decision making in industrial design practice. Despite the significant progress that has been made in this area, a number of challenges remain to be overcome in order to fully realize the potential of computation-based design workflows to accelerate complex systems design. One of the main challenges is computational cost. With the increasing push towards high-fidelity models, evaluation of a wide range of design alternatives/concepts can be computationally infeasible even on massively parallel computers. Another challenge arises from the fact that uncertainty is ubiquitous in the mathematical modeling and characterization of engineering systems. Existing approaches can be computationally prohibitive when dealing with uncertainty, e.g., when engineers wish to predict performance statistics using a high-fidelity model and/or decide on parameter settings that can help mitigate the impact of uncertainty. These two challenges are exacerbated by the fact that computer models of complex real-world engineering systems are often parametrized in terms of a huge number of design variables and uncertain parameters. There is a pressing need for scalable and efficient algorithms to tackle high-dimensional problems in computational modeling and design optimization, especially in the presence of uncertainty.