A computer-implemented method for enabling control or monitoring of a computer-controlled entity operating in an environment by predicting a future state of the computer-controlled entity and/or its environment using sensor data which is indicative of a current state of the computer-controlled entity and/or its environment, the method comprising: - receiving sensor data indicative of the current state of the computer-controlled entity and/or its environment; - accessing a first trained neural network for approximating a drift component of a stochastic differential equation and a second trained neural network for approximating a diffusion component of the stochastic differential equation; - discretizing the stochastic differential equation into time steps, wherein the discretized stochastic differential equation defines a second state of the computer-controlled entity and/or its environment as being dependent on the current state of the computer-controlled entity and a time interval between a time step k of the current state and a time step k+1 of the second state; - obtaining a time-evolving mean function and a time-evolving covariance function based on the discretization, wherein at any time t, a state of the computer-controlled entity and/or its environment is expressed as a distribution defined by a mean at time t and a covariance at time t; - obtaining a probability distribution of the current state of the computer-controlled entity and/or its environment, the probability distribution being defined by a current mean and a current covariance; - determining a transition probability distribution of the second state of the computer-controlled entity and/or its environment by: - estimating a mean for the second state by expanding the time-evolving mean function, using the current mean, - estimating a covariance for the second state using the estimated mean for the second state and the current covariance. - receiving sensor data indicative of the current state of the computer-controlled entity and/or its environment; - accessing a first trained neural network for approximating a drift component of a stochastic differential equation and a second trained neural network for approximating a diffusion component of the stochastic differential equation; - discretizing the stochastic differential equation into time steps, wherein the discretized stochastic differential equation defines a second state of the computer-controlled entity and/or its environment as being dependent on the current state of the computer-controlled entity and a time interval between a time step k of the current state and a time step k+1 of the second state; - obtaining a time-evolving mean function and a time-evolving covariance function based on the discretization, wherein at any time t, a state of the computer-controlled entity and/or its environment is expressed as a distribution defined by a mean at time t and a covariance at time t; - obtaining a probability distribution of the current state of the computer-controlled entity and/or its environment, the probability distribution being defined by a current mean and a current covariance; - determining a transition probability distribution of the second state of the computer-controlled entity and/or its environment by: - estimating a mean for the second state by expanding the time-evolving mean function, using the current mean, - estimating a covariance for the second state using the estimated mean for the second state and the current covariance. - estimating a mean for the second state by expanding the time-evolving mean function, using the current mean, - estimating a covariance for the second state using the estimated mean for the second state and the current covariance.