Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models (Papers Track)
Yixuan Sun (Argonne National Laboratory); Elizabeth Cucuzzella (Tufts University); Steven Brus (Argonne National Laboratory); Sri Hari Krishna Narayanan (Argonne National Laboratory); Balu Nadiga (Los Alamos National Lab); Luke Van Roekel (Los Alamos National Laboratory); Jan Hückelheim (Argonne National Laboratory); Sandeep Madireddy (Argonne National Laboratory)
Abstract
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generate perturbed parameter ensemble data and generate surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.