Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling (Papers Track)
Yixuan Sun (Argonne National Laboratory); Romain Egele (Oak Ridge National Laboratory); Sri Hari Krishna Narayanan (Argonne National Laboratory); Luke Van Roekel (Los Alamos National Laboratory); Carmelo Gonzales (NIVDIA); Steven Brus (Argonne National Laboratory); Balu Nadiga (Los Alamos National Laboratory); Sandeep Madireddy (Argonne National Laboratory); Prasanna Balaprakash (Oak Ridge National Laboratory)
Abstract
Accurate simulations of the oceans are crucial in understanding the Earth system. Despite their efficiency, simulations at lower resolutions must rely on various uncertain parameterizations to account for unresolved processes. However, model sensitivity to parameterizations is difficult to quantify, making it challenging to tune these parameterizations to reproduce observations. Deep learning surrogates have shown promise for efficient computation of the parametric sensitivities in the form of partial derivatives, but their reliability is difficult to evaluate without ground truth derivatives. In this work, we leverage large-scale hyperparameter search and ensemble learning to improve both forward predictions, autoregressive rollout, and backward adjoint sensitivity estimation. Particularly, the ensemble method provides epistemic uncertainty of function value predictions and their derivatives, providing improved reliability of the neural surrogates in decision making.