US 2026/0087319 A1 · CONVOLUTION HIDENN-TENSOR DECOMPOSITION FOR MANUFACTURING SIMULATION, PERFORMANCE PREDICTION, AND TOPOLOGY OPTIMIZATION OF MULTISCALE MATERIAL SYSTEMS

Abstract

Convolution-Hierarchical Deep-learning Neural Network-Tensor decomposition (C-HiDeNN-TD) has four features: (1) Tensor decomposition breaking down the whole design into small tractable problems; (2) Convolution built-in filter to increase accuracy without adding extra DoFs; (3) Convolution built-in filter can avoid checkerboard pattern; (4) Design can have arbitrary smoothness without adding extra DoFs.

Claims19

Dependent claims

A forward engineering analysis method of Convolution-Hierarchical Deep-learning Neural Network-Tensor Decomposition (C-HiDeNN-TD) for solving a physics problem using more than one graphics processing units (GPUs) or tensor processing units (TPU) combined with multiple CPUs, comprising:

(1) providing a plurality of modes and at least one C-HiDeNN parameter;
(2) computing at least one C-HiDeNN function for each of the plurality of modes based on the at least one C-HiDeNN parameter;
(3) solving the physics problem for each of the plurality of modes based on the C-HiDeNN function;
wherein the at least one C-HiDeNN function comprises a spatial C-HiDeNN function;
wherein the C-HiDeNN parameter comprises at least one of a patch size s, a dilation parameter a, and a reproducing order p.

2 more elements

Dependencies are inferred from the claim text.

Convolution hidenn-tensor decomposition for manufacturing simulation, performance prediction, and topology optimization of multiscale material systems

Life of this patent

Convolution hidenn-tensor decomposition for manufacturing simulation, performance prediction, and topology optimization of multiscale material systems

Life of this patent

Abstract

Claims19