Session

DescriptionFusion energy is a grand challenge that can contribute to reducing greenhouse gas emissions and its negative effects on climate change. The successful deployment of fusion energy devices will depend on the robust engineering design of every component. To fully understand the complexities of fusion energy systems, a multidisciplinary approach is needed. Regardless of the form it takes, a fusion energy device encompasses a wide array of length and time scales that will require novel computational techniques to bridge the gaps existing between the multiple scales and multiple physics. One avenue for addressing the fusion energy challenge is with high-fidelity simulations that are used to inform machine learning and AI algorithms to produce reduced-order models. These computational models accurately capture the physics while providing fast-running macro-scale, or engineering-scale, simulations for rigorous design optimizations. In this minisymposium, experts in modeling and simulation are brought together for fusion energy applications to lay out what is needed to build these simulations. The focus will be on bridging the gap that exists between high-fidelity modeling and simulation, and engineering models.