Patent ID: 11886782
Assignee: ROBERT BOSCH GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. A computer-implemented method for training a dynamics model to learn dynamics of a physical system by learning to infer a future state of the physical system and/or its environment based on a current state of the physical system and/or its environment, the method comprising the following steps:
accessing training data representing a time-series of states of the physical system and/or its environment, and model data defining a machine learnable dynamics model which includes a machine learnable Lyapunov function;
learning the dynamics model based on time-sequential pairs of states so as to learn to infer a future state of the physical system and/or its environment based on the current state, wherein the learning includes:
constraining the learning to provide a globally stable modelling of the dynamics of the physical system by jointly learning the dynamics model and the Lyapunov function so that values of the learned Lyapunov function decrease along all trajectories of states inferred by the learned dynamics model;

wherein the machine learnable Lyapunov function is represented at least in part by an input-convex neural network.