Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models

• URL: http://arxiv.org/abs/2311.08421v1
• Date: Fri, 10 Nov 2023 16:37:43 GMT
• Title: Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
• Authors: Yixuan Sun, Elizabeth Cucuzzella, Steven Brus, Sri Hari Krishna Narayanan, Balu Nadiga, Luke Van Roekel, Jan H\"uckelheim, Sandeep Madireddy
• Abstract summary: Ocean processes affect phenomena such as hurricanes and droughts. For an idealized ocean model, we generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.
• Score: 2.956865819041394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 generated perturbed parameter ensemble data and trained surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.

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