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The solution of the path structured multimarginal Schr\"{o}dinger bridgeproblem (MSBP) is the most-likely measure-valued trajectory consistent with asequence of observed probability measures or distributional snapshots. Weleverage recent algorithmic advances in solving such structured MSBPs forlearning stochastic hardware resource usage by control software. The solutionenables predicting the time-varying distribution of hardware resourceavailability at a desired time with guaranteed linear convergence. Wedemonstrate the efficacy of our probabilistic learning approach in a modelpredictive control software execution case study. The method exhibits rapidconvergence to an accurate prediction of hardware resource utilization of thecontroller. The method can be broadly applied to any software to predictcyber-physical context-dependent performance at arbitrary time.

Path Structured Multimarginal Schr\"odinger Bridge for Probabilistic Learning of Hardware Resource Usage by Control Software