Ausgabe 3/2023
Special Issue: Machine Learning Theories, Modeling, and Applications to Computational Materials Science, Additive Manufacturing, Mechanics of Materials, Design and Optimization, Volume III : Digital Twins and Inverse problem Solutions / Guest Edited by Wing Kam Liu, Nathaniel Trask, Shaofan Li
Inhalt (12 Artikel)
A machine learning-based probabilistic computational framework for uncertainty quantification of actuation of clustered tensegrity structures
Yipeng Ge, Zigang He, Shaofan Li, Liang Zhang, Litao Shi
Semi-supervised invertible neural operators for Bayesian inverse problems
Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
On the geometry transferability of the hybrid iterative numerical solver for differential equations
Adar Kahana, Enrui Zhang, Somdatta Goswami, George Karniadakis, Rishikesh Ranade, Jay Pathak
An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models
Anh Tran, Hojun Lim
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification
Shuheng Liao, Tianju Xue, Jihoon Jeong, Samantha Webster, Kornel Ehmann, Jian Cao
Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials
Hongwei Guo, Xiaoying Zhuang, Xiaolong Fu, Yunzheng Zhu, Timon Rabczuk
Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation
Carianne Martinez, Dan S. Bolintineanu, Aaron Olson, Theron Rodgers, Brendan Donohoe, Kevin M. Potter, Scott A. Roberts, Reeju Pokharel, Stephanie Forrest, Nathan W. Moore
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
Quercus Hernández, Alberto Badías, Francisco Chinesta, Elías Cueto
Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading
Minglei Lu, Ali Mohammadi, Zhaoxu Meng, Xuhui Meng, Gang Li, Zhen Li
A thermodynamics-informed active learning approach to perception and reasoning about fluids
Beatriz Moya, Alberto Badías, David González, Francisco Chinesta, Elías Cueto
Machine learning meta-models for fast parameter identification of the lattice discrete particle model
Yuhui Lyu, Madura Pathirage, Elham Ramyar, Wing Kam Liu, Gianluca Cusatis