Closed-Loop Adaptive Monte Carlo Framework for Uncertainty Forecasting in Nonlinear Dynamic Systems

What is it about?

Monte Carlo simulations are commonly used for forecasting applications and predictive analytics in nonlinear dynamic systems. However, they do not come with guarantees of how good the generated forecast of the system is. We develop a closed-loop architecture for Monte Carlo that performs adaptations to ensure that the forecasting accuracy in terms of well defined quantities of interest are within prescribed bounds for the entire forecasting window.

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