Techniques for training a machine learning model to modify portions of shapes when generating designs for three-dimensional objects

One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to train a machine learning model to modify portions of shapes when designing three-dimensional (“3D”) objects by performing the steps of:

• converting first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution, wherein the first structural analysis data comprises at least one of one or more strain energy values, one or more displacements, or one or more rotations, and wherein the first coarse structural analysis data comprises a downsampled version of the at least one of one or more strain energy values, one or more displacements, or one or more rotations at the second resolution;
• generating one or more training sets based on a first shape, the first coarse structural analysis data, and a second shape that is derived from the first shape, wherein each of the one or more training sets is associated with a different portion of the first shape;
• and
• performing one or more machine learning operations on the machine learning model using the one or more training sets to generate a first trained machine learning model that is trained to modify at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.

A system comprising:

• one or more memories storing instructions;
• and
• one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
• converting first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution, wherein the first structural analysis data comprises at least one of one or more strain energy values, one or more displacements, or one or more rotations, and wherein the first coarse structural analysis data comprises a downsampled version of the at least one of one or more strain energy values, one or more displacements, or one or more rotations at the second resolution;
• generating one or more training sets based on a first shape associated with the shape definition, the first coarse structural analysis data, and a second shape that is derived from the first shape, wherein each of the one or more training sets is associated with a different portion of the first shape;
• and
• performing one or more machine learning operations on a machine learning model using the one or more training sets to generate a first trained machine learning model that is trained to modify at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.