摘要
The inverse engineering problems approach is a discipline that is growing very rapidly. The inverse problems we consider here concern the way to determine the state and/or parameters of the physical system of interest using observed measurements. In this context the filtering algorithms constitute a key tool to offer improvements of our knowledge on the system state, its forecast… which are essential, in particular, for oceanographic and meteorologic operational systems. The objective of this paper is to give an overview on how one can design a simple, no time-consuming Reduced-Order Adaptive Filter (ROAF) to solve the inverse engineering problems with high forecasting performance in very high dimensional environment.
The inverse engineering problems approach is a discipline that is growing very rapidly. The inverse problems we consider here concern the way to determine the state and/or parameters of the physical system of interest using observed measurements. In this context the filtering algorithms constitute a key tool to offer improvements of our knowledge on the system state, its forecast… which are essential, in particular, for oceanographic and meteorologic operational systems. The objective of this paper is to give an overview on how one can design a simple, no time-consuming Reduced-Order Adaptive Filter (ROAF) to solve the inverse engineering problems with high forecasting performance in very high dimensional environment.
