Generating and analyzing material structures based on neural networks
AssigneeVolkswagen Aktiengesellschaft
InventorsWesley Teskey, Gianina Alina Negoita
Life of this patent
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Abstract
In one embodiment, a method is provided. The method includes obtaining a set of graphs for a set of material structures. Each graph of the set of graphs is associated with a material structure of the set of material structures. The method also includes determining first sets of stress values and first sets of strain values for the set of graphs based on a neural network. The method further includes obtaining second sets of stress value and second sets of strain values for a subset of the set of material structures. The method further includes updating the neural network based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values.
Claims20
A method, comprising:
- obtaining a set of graphs for a set of material structures, wherein:
- each graph of the set of graphs is associated with a material structure of the set of material structures;
- each graph comprises nodes representing finite elements of a corresponding material structure;
- determining first sets of stress values and first sets of strain values for the set of graphs based on a machine learning model;
- obtaining second sets of stress value and second sets of strain values for a subset of the set of material structures;
- updating the machine learning model based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values;
- and
- determining other stress values and other strain values for other material structures based on the updated machine learning model.
An apparatus, comprising:
- a memory configured to store data;
- and
- a processing device coupled to the memory, the processing device configured to:
- obtaining a set of graphs for a set of material structures, wherein:
- each graph of the set of graphs is associated with a material structure of the set of material structures;
- each graph comprises nodes representing finite elements of a corresponding material structure;
- determine first sets of stress values and first sets of strain values for the set of graphs based on a machine learning model;
- obtain second sets of stress value and second sets of strain values for a subset of the set of material structures;
- update the machine learning model based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values;
- determining other stress values and other strain values for other material structures based on the updated machine learning model.
A non-transitory computer-readable storage medium including instructions that, when executed by a processing device, cause the processing device to perform operations comprising:
- obtain a set of graphs for a set of material structures, wherein:
- each graph of the set of graphs is associated with a material structure of the set of material structures;
- each graph comprises nodes representing finite elements of a corresponding material structure;
- determine first sets of stress values and first sets of strain values for the set of graphs based on a machine learning model;
- obtain second sets of stress value and second sets of strain values for a subset of the set of material structures;
- update the machine learning model based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values;
- and
- determining other stress values and other strain values for other material structures based on the updated machine learning model.
Dependencies are inferred from the claim text.