Abstract: A device and computer-implemented method for predicting a state of a technical system. A state of the technical system is detected and a time series is provided which comprises values which characterize a course of the detected state of the technical system. Using a learning-based model for predicting the short-term behavior of the technical system, a first value for the prediction is determined as a function of the values of the time series, and, using a physical model for predicting the long-term behavior of the technical system, a second value for the prediction is determined as a function of the values of the time series, and wherein a value of the prediction is determined as a function of the first value for the prediction and the second value for the prediction.

Abstract: A device and computer-implemented method for predicting a state of a technical system. A state of the technical system is detected. A time series is provided which includes values which characterize a course of the detected state of the technical system. Using a first filter, first filtered values for predicting the short-term behavior of the technical system are determined as a function of the values of the time series. Using a second filter, second filtered values for predicting the long-term behavior of the technical system are determined as a function of the values of the time series. A first value for the prediction is determined as a function of the filtered first values. A second value for the prediction is determined as a function of the filtered second values. A value of the prediction is determined as a function of the first and second values for the prediction.

Abstract: A computer-implemented method for training a neural network including a neural ordinary differential equation (ODE) block. A first ODE solver may be used to train the neural ODE block. A second ODE solver may be used to train and verify that the neural ODE block describes an ODE as an ODE flow. During a forward pass of an iteration of training, a first performance value is obtained by applying the first ODE solver to the neural ODE block and a second performance value is obtained by applying the second ODE solver to the neural ODE block. An accuracy parameter of the first ODE solver is adjusted based on the difference between the first performance value and the second performance value.