ICON is shown to compute the posterior predictive mean of differential equation solutions, and a generative extension, GenICON, provides samples from this distribution for uncertainty quantification.
Katsoulakis, and Luc Rey-Bellet
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Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
ICON is shown to compute the posterior predictive mean of differential equation solutions, and a generative extension, GenICON, provides samples from this distribution for uncertainty quantification.