Method for adaptation of a surrogate model describing a dynamic system and its application to system optimization

A system for enabling adaptations of an existing machine learning (ML), model describing a dynamic physical system governed by partial differential equations (PDEs), the system comprising one or more processors that, alone or in combination, are configured to provide for the execution of the following steps:

• building a neural network that is composed of a main neural network modelling the dynamic physical system and a parameter-embedding module for embedding system parameters of the PDEs of the dynamic physical system;
• using the parameter-embedding module to train the main neural network over a dataset including a set of experimental and/or simulation data collected over different system parameter configurations;
• and
• using the trained neural network for predicting an evolution of the dynamic physical system based on a new, yet unseen underlying system parameter configuration.

A tangible, non-transitory computer-readable medium having instructions thereon which, upon being executed by one or more processors, alone or in combination, provide for execution of a method enabling adaptations of an existing machine learning (ML) model describing a dynamic physical system governed by partial differential equations (PDEs), the method comprising:

• building a neural network that is composed of a main neural network modelling the dynamic physical system and a parameter-embedding module for embedding system parameters of the PDEs of the dynamic physical system;
• using the parameter-embedding module to train the main neural network over a dataset including a set of experimental and/or simulation data collected over different system parameter configurations;
• and
• using the trained neural network for predicting an evolution of the dynamic physical system based on a new, yet unseen underlying system parameter configuration.