Abstract: Techniques for predicting a failure metric of a physical system using a semiparametric model, including providing raw data representative of the physical system, to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred. A parametric and a nonparametric component of the semiparametric model are estimated and a hazard rate is predicted at a given time with the semiparametric model.

Abstract: Techniques for predicting a failure metric of a physical system using a semiparametric model, including providing raw data representative of the physical system, to identify a set of units at risk in the physical system, a set of times of treatment corresponding to a event of at least one unit in the set of units, and an index-set of the at least one unit for which a event has occurred. A parametric and a nonparametric component of the semiparametric model are estimated and a hazard rate is predicted at a given time with the semiparametric model.

Abstract: Techniques for generating a dynamic treatment control policy for a cyber-physical system having one or more components, including a data collector for collecting data representative of the cyber-physical system, and adaptive stochastic controller including one or more models for generating a predicted value corresponding to available actions based on an objective function, and an approximate dynamic programming element configured to receive actual operation metrics corresponding to the available actions. The approximate dynamic programming element can learn a state-action map and generate a dynamic treatment control policy using the one or more models.