LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling of Short Fiber-Reinforced Composites

no code implementations • 6 Jan 2023 • Haoyan Wei, C. T. Wu, Wei Hu, Tung-Huan Su, Hitoshi Oura, Masato Nishi, Tadashi Naito, Stan Chung, Leo Shen

In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC.

Transfer Learning

Intelligent multiscale simulation based on process-guided composite database

no code implementations • 20 Mar 2020 • Zeliang Liu, Haoyan Wei, Tianyu Huang, C. T. Wu

In the paper, we present an integrated data-driven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale simulation.

BIG-bench Machine Learning Transfer Learning

Exploring the 3D architectures of deep material network in data-driven multiscale mechanics

no code implementations • 2 Jan 2019 • Zeliang Liu, C. T. Wu

This paper extends the deep material network (DMN) proposed by Liu et al. (2019) to tackle general 3-dimensional (3D) problems with arbitrary material and geometric nonlinearities.

Model Compression