AI-Assisted Parameter Tuning Will Speed Development and Clarify Uncertainty in E3SM
Focal Area: This idea is best aligned with predictive modeling. It targets Earth System Model improvement and uncertainty quantification
54 ENVIRONMENTAL SCIENCES↗
Engineering topics
Marathe, Aniruddha
Publications and source records attributed to Marathe, Aniruddha.
GANpiler
Focal Area(s): Rather than augment or replace physical models with machine learning, we instead preserve the existing models and augment the underlying code with faster surrogate models created by generative adversarial networks (GAN). To leverage the performance optimization, we also propose a runtime system and user interfaces that allow prediction and tracking of accumulated error as well as dynamic, per-process decision making as to which model (if any) to use for each iteration. Science Challenge: This proposal sits at the nexus of two hard problems. First, climate models based on machine learning will be, by their nature, difficult to trust once their predictions begin diverging from the consensus. Second, compilers and hardware have been making only incremental performance gains for decades. GPGPUs have provided a welcome performance boost for codes that can take advantage of them, but there is no similar technology on the horizon to provide the next performance leap.
54 ENVIRONMENTAL SCIENCES↗