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Status: Unchecked ×Keyword: hybrid physics-data models ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Arctic and Antarctic ice dynamics

   Hybrid physics-data-driven modeling for sea ice thermodynamics and transfer learning

   De Cillis, Carrassi, Brajard et al. · arXiv (Cornell University) · 2026

   The study builds hybrid sea-ice column models in which a neural network corrects forecast errors, tests them over long forecasts, explores transfer learning between model set-ups, and analyses which inputs matter.

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   1. UncheckedIn this study, neural-network hybrid sea-ice models stayed stable, tolerated errors in starting conditions and weather forcing, and beat a climatology-based benchmark.“The NN-based hybrids prove to be stable, robust to initial condition and atmospheric forcing errors, and consistently outperform their climatology-based counterpart.”
   2. UncheckedTesting a pretrained network directly on a new task shows how adaptable it is, so transfer learning is advised when it beats a trivial baseline.“Results indicate that direct evaluation of pretrained networks on the target task provides useful insights into their adaptability, recommending transfer learning whenever performance exceeds a trivial baseline.”