Autodesk, Inc.

Lee Morris Taylor

Morgan Fabian

Ran Zhang

Ebot Etchu Ndip-Agbor

In various embodiments, a training application trains a machine learning model to modify portions of shapes when designing 3D objects. The training application converts 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. Subsequently, the training application generates 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. Each training set is associated with a different portion of the first shape. The training application then performs one or more machine learning operations on the machine learning model using the training set(s) to generate a trained machine learning model. The trained machine learning model modifies at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.

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

A method, apparatus, and system provide the ability to simulate dynamic motion for a computerized model (of finite mesh elements). An element diagonal lumped mass matrix of the mesh, an estimate of a highest element eigenvector and eigenvalue of the mesh, and a kinematic state of the model are computed. Processing iterates until exceeding a time duration. Incremental strain and stress tensors, and hypo-elastic material constants are computed. Within the time duration iteration, eigenvalues are converged, a power-sweep stress field is computed from the strain field using the material constants; divergence of the power-sweep stress field is computed using the current gradient operator; and a power-sweep estimate is computed. Upon convergence, the stability limit is determined and utilized as a time interval for simulating the dynamic motion.

Method and apparatus for explicit simulation

In various embodiments, a topology optimization application solves a topology optimization problem associated with designing a three-dimensional ('3D') object. The topology optimization application coverts a first shape having a first resolution and representing the 3D object to a coarse shape having a second resolution that is lower than the first resolution. Subsequently, the topology optimization application computes coarse structural analysis data based on the coarse shape. The topology optimization application then uses a trained machine learning model to generate a second shape having the first resolution and representing the 3D object based on the first shape and the coarse structural analysis data. The trained machine learning model modifies a portion of a shape having the first resolution based on structural analysis data having the second resolution. Advantageously, generating the second shape based on structural analysis data having a lower resolution reduces computational complexity relative to prior art techniques.

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Cedar Crest, NM, United States of America

Lee Morris Taylor