Joint work with Simon Scheidegger (University of Lausanne)

There is a  growing demand to quantify parametric uncertainty as well as economic and climate uncertainty on the climate policies to tackle global warming. To investigate parametric uncertainty and nonlinear interactions among the uncertain model parameters, as well as learning about the climate equilibrium sensitivity, we develop a high-dimensional stochastic climate-economy model that propagates parametric uncertainty as pseudo-states. We approximate all equilibrium functions using deep equilibrium nets, which we show to be up to $mathcalO({10}^5)$ faster than other state-of-the-art methods. To limit the number of model evaluations to obtain convergent statistics,  we further interpolate the outcomes of the cheap-to-evaluate surrogate model employing  Gaussian process regression in combination with  Bayesian active learning,  from which we estimate the  Sobol'  indices and univariate effects.