Design of Physics-Aware Neural Networks: Symmetry constraints

Project Description

The project aims at the development of Physics-Aware Neural Networks (PANN) accounting for symmetry constraints. These enable the user to include physics into the NN models in a straight-forward fashion. Within this overarching objective, the project has the following goals:

  • Collect and develop NN modelling strategies for strain energies with symmetry constraints;
  • Develop a list of input features for qualitative and quantitative description of the information available for the problem at hand considering the computational environment. Such a list can include type of symmetry, available training resources, strictness of symmetry constraints and so on;
  • Implement and evaluate different NN models;
  • Evaluate the influence of input parameters on output features, such as accuracy of symmetry of the surrogate and required number of samples vs. accuracy of the model;
  • Develop a modelling language for PANN-Sym;
  • Apply the model to single crystals and in multiscale modelling of hyper-elastic solids.

The project also considers more specific intermediate goals targetting independent student research projects on the B.Sc./M.Sc. level.

Project Information

Project Number TF-A-1
Project Name Design of Physics-Aware Neural Networks: Symmetry constraints
Project Duration November 2021 - December 2023
Project Leader Felix Fritzen
Steffen Staab
Project Members Renan Peirera Alessio, PhD Researcher
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