Inferring low-dimensional microstructure representations using convolutional neural networks

8 November 2016

Papers citing "Inferring low-dimensional microstructure representations using convolutional neural networks"

6 / 6 papers shown

Title
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks Julian Lißner F. Fritzen BDL 24 2 0 24 Feb 2023
Towards Microstructural State Variables in Materials Systems V. Sundararaghavan Megna N. Shah Jeff P. Simmons AI4CE 24 0 0 11 Jan 2023
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions Joseph B. Choi Phong C. H. Nguyen O. Sen H. Udaykumar Stephen Seung-Yeob Baek PINN AI4CE 29 11 0 15 Nov 2022
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science Charlotte Loh T. Christensen Rumen Dangovski Samuel Kim Marin Soljacic 37 17 0 15 Oct 2021
An efficient optimization based microstructure reconstruction approach with multiple loss functions Anindya Bhaduri Ashwini Gupta Audrey Olivier L. Graham‐Brady 38 29 0 04 Feb 2021
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel Brian L. DeCost Bo Lei T. Francis Elizabeth A. Holm AI4CE 25 143 0 04 May 2018