Session

DescriptionThe complexity of real-world physical phenomena, combined with the need to address intricate engineering challenges, has prompted the scientific community to develop numerical methods and models for simulating large-scale multiphysics phenomena using high-performance computing resources. Multiphysics simulations are increasingly utilized in complex systems and emerging technologies, such as nanoscale materials and biophysics. These simulations present significant challenges, mainly due to the intricacies of modeling multiple physical processes that interact in a coupled manner or occur across varying spatial and temporal scales. Establishing robust coupling between diverse solvers and algorithms is particularly demanding, requiring innovative computational strategies to achieve stability, accuracy, and convergence. In multiphysics coupling scenarios, the boundaries between disciplines often merge, emphasizing the need for interdisciplinary collaboration. This minisymposium brings together experts in computational engineering and physics to explore various domains and applications. Four speakers will present specific coupled science cases, highlighting computational challenges, advancements toward exascale readiness, and the future role of machine learning as full surrogate models or as complementary tools to established numerical methods.