Session

Description Large scale scientific computing is increasingly pervasive in many research fields and industrial applications, such as: biological modeling of coupled soft-tissue systems, computational fluid dynamics and turbulence, and tsunami inundation via shallow water equations. Not only can forward simulation of these problems be prohibitively expensive, but additional computation and storage of gradient or adjoint information further compounds such numerical burden. This matter becomes further complicated when conducting an inverse or optimal control problem, which requires many such forward simulations and can suffer from the curse of dimensionality. Optimization and control problems can alleviate this burden somewhat via reduced-order surrogate models, randomized compression techniques, neural network surrogates designed to imitate dynamics or operators. The goal of this minisymposium is to bring together researchers working on finite- and infinite-dimensional control and simulation to discuss new methodologies for analyzing and solving problems in the extreme computing regime. It focuses on new techniques in model reduction for high-fidelity physics simulations; surrogate modeling techniques based on learning; distributed and multilevel optimization on HPCs; compression and storage with both deterministic and randomized methods; nonsmooth optimization; and adaptive discretizations.