A system for enabling control of a physical system based on a state of the physical system which is inferred from sensor data, comprising: - an input interface for accessing sensor data representing sensor measurements (y 1:y T) associated with the state of the physical system; - an output interface to an output device which is used in the control of the physical system; - a processor subsystem configured to iteratively infer the state of the physical system based on the sensor data by, in an iteration: - using the input interface, obtain a sensor measurement (y T); - obtain an initial inference of the state using a mathematical model representing a prior knowledge-based modelling of the state as a function of the sensor measurement (y T) and a previous inferred state (x T); - input the initial inference of the state and the sensor measurement (y T) into a learned model, wherein the learned model has been learned to minimize an error between initial inferences provided by the mathematical model and a ground truth and to provide a correction value (e T+1) as output for correcting the initial inference of the state of the mathematical model; and - obtain a current inferred state (x T+1) by combining the initial inference of the state with the correction value (e T+1); wherein the processor subsystem is further configured to, using the output interface, provide output data to the output device to enable the control of the physical system based on the current inferred state (x T+1). - an input interface for accessing sensor data representing sensor measurements (y 1:y T) associated with the state of the physical system; - an output interface to an output device which is used in the control of the physical system; - a processor subsystem configured to iteratively infer the state of the physical system based on the sensor data by, in an iteration: - using the input interface, obtain a sensor measurement (y T); - obtain an initial inference of the state using a mathematical model representing a prior knowledge-based modelling of the state as a function of the sensor measurement (y T) and a previous inferred state (x T); - input the initial inference of the state and the sensor measurement (y T) into a learned model, wherein the learned model has been learned to minimize an error between initial inferences provided by the mathematical model and a ground truth and to provide a correction value (e T+1) as output for correcting the initial inference of the state of the mathematical model; and - obtain a current inferred state (x T+1) by combining the initial inference of the state with the correction value (e T+1); - using the input interface, obtain a sensor measurement (y T); - obtain an initial inference of the state using a mathematical model representing a prior knowledge-based modelling of the state as a function of the sensor measurement (y T) and a previous inferred state (x T); - input the initial inference of the state and the sensor measurement (y T) into a learned model, wherein the learned model has been learned to minimize an error between initial inferences provided by the mathematical model and a ground truth and to provide a correction value (e T+1) as output for correcting the initial inference of the state of the mathematical model; and - obtain a current inferred state (x T+1) by combining the initial inference of the state with the correction value (e T+1);