A control method for controlling an operation of a system , the system being an air-conditioning system comprising ventilation units , an airflow sensor and a temperature sensor , the system having non-linear dynamics represented by partial differential equations (PDEs), the control method including a computer-implemented method of training a neural network model for controlling the operation of the system having non-linear dynamics represented by partial differential equations (PDEs), the computer-implemented method of training the neural network model comprising: collecting a digital representation of time series data indicative of measurements of the operation of the system at different instances of time; and training the neural network model having an autoencoder architecture including an encoder configured to encode the digital representation into a latent space, a linear predictor configured to propagate the encoded digital representation into the latent space with linear transformation determined by values of parameters of the linear predictor , and a decoder configured to decode the linearly transformed encoded digital representation to minimize a loss function including a prediction error between outputs of the neural network model decoding measurements of the operation at an instant of time and measurements of the operation collected at a subsequent instance of time, and a residual factor of the PDE having eigenvalues dependent on the parameters of the linear predictor , wherein the residual factor of the PDE is based on a Lie operator, the computer-implemented method further comprising performing eigen-decomposition to the Lie operator; the control method further comprising controlling the system to control a temperature and an airflow in a room through the ventilation units , by using a linear control law including a control matrix formed by the values of the parameters of the linear predictor . collecting a digital representation of time series data indicative of measurements of the operation of the system at different instances of time; and training the neural network model having an autoencoder architecture including an encoder configured to encode the digital representation into a latent space, a linear predictor configured to propagate the encoded digital representation into the latent space with linear transformation determined by values of parameters of the linear predictor , and a decoder configured to decode the linearly transformed encoded digital representation to minimize a loss function including a prediction error between outputs of the neural network model decoding measurements of the operation at an instant of time and measurements of the operation collected at a subsequent instance of time, and a residual factor of the PDE having eigenvalues dependent on the parameters of the linear predictor , wherein the residual factor of the PDE is based on a Lie operator, the computer-implemented method further comprising performing eigen-decomposition to the Lie operator; the control method further comprising controlling the system to control a temperature and an airflow in a room through the ventilation units , by using a linear control law including a control matrix formed by the values of the parameters of the linear predictor . <EOS>