AI RESEARCH
Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics
arXiv CS.LG
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ArXi:2605.20580v1 Announce Type: new This work explores a dynamics-informed Temporal Fusion Transformer (TFT) as a data-driven surrogate for computationally intensive Earth system simulations. Focusing on multivariate time series describing global ocean transport, we nstrate the surrogate's ability to forecast tip events across thousands of time steps. The data involve up to 21 non-stationary time series in addition to static covariates describing free parameters and initial conditions.