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Special Collection on Machine Learning Enabled Modeling and Discovery for Engineering Mechanics
Guest Editors:
Krishna Garikipati, Ph.D., University of Michigan
Paris Perdikaris, Ph.D., University of Pennsylvania
Mazdak Tootkaboni, Ph.D., University of Massachusetts Dartmouth
Nathaniel Trask, Ph.D., Sandia National Laboratories
WaiChing Sun, Ph.D., Columbia University
Driven by the demonstrated success of machine learning (ML) in many scientific domains and the demands to efficiently handle a wide spectrum of data types, sources, and dimensions, ML and data-driven techniques have become pervasive throughout the computational physics and mechanics literature. The ability of ML models to approximate functions, solve differential equations, a analyze high-dimensional data, and handle repetitive tasks, have made them an attractive option to augment or replace existing paradigms for computational mechanics. However, unlike the conventional modeling and simulation tools where interpretability and compatibility of known physics principles are guaranteed, machine learning techniques for mechanics and multiscale modeling can be difficult to interpret, difficult to train, and the learned models may violate physics principles, such as invariance and equivariance, and therefore are not always trustworthy for high-consequence engineering applications. This special issue welcomes novel contributions that measurably advance the state-of-the-art in engineering mechanics.
Papers in this Collection
Physics-Informed Neural Network Solution of Thermo–Hydro–Mechanical Processes in Porous Media
Danial Amini;
Ehsan Haghighat; and Ruben Juanes
Published online: September 15, 2022
Data-Driven Modal Equivalent Standardization for Early Damage Detection in Bridge Structural Health Monitoring
Zhen Wang, S.M.ASCE; Ting-Hua Yi, M.ASCE; Dong-Hui Yang, M.ASCE; and Hong-Nan Li, F.ASCE
Published online: October 25, 2022
DeepFEM: A Novel Element-Based Deep Learning Approach for Solving Nonlinear Partial Differential Equations in Computational Solid Mechanics
Yijia Dong; Tao Liu; Zhimin Li; and
Pizhong Qiao, F.ASCE
Published online: November 17, 2022
SenseNet: A Physics-Informed Deep Learning Model for Shape Sensing
Yitao Qiu;
Prajwal Kammardi Arunachala; and
Christian Linder
Published online: January 5, 2023
LS-DYNA Machine Learning–Based Multiscale Method for Nonlinear Modeling of Short Fiber–Reinforced Composites
Haoyan Wei; C. T. Wu; Wei Hu; Tung-Huan Su; Hitoshi Oura; Masato Nishi; Tadashi Naito; Stan Chung; and Leo Shen
Published online: January 5, 2023
Efficient Data-Driven Modeling of Nonlinear Dynamical Systems via Metalearning
Shanwu Li; and
Yongchao Yang, M.ASCE
Published online: January 12, 2023
CNN-Based Surrogate for the Phase Field Damage Model: Generalization across Microstructure Parameters for Composite Materials
Yuxiang Gao; Matthew Berger; and
Ravindra Duddu
Published online: March 16, 2023
Accelerated Design of Architected Materials with Multifidelity Bayesian Optimization
Chengyang Mo, Ph.D.; Paris Perdikaris, Ph.D.; and
Jordan R. Raney, Ph.D.
Published online: March 30, 2023
Bayesian Nonlocal Operator Regression: A Data-Driven Learning Framework of Nonlocal Models with Uncertainty Quantification
Yiming Fan;
Marta D’Elia; Yue Yu;
Habib N. Najm; and Stewart Silling
Published online: May 26, 2023
Physics-Informed Neural Networks for System Identification of Structural Systems with a Multiphysics Damping Model
Tong Liu; and
Hadi Meidani, Ph.D.
Published online: August 2, 2023
Formulation of Constitutive Viscoelastic Properties of Modified Bitumen Mastic Using Genetic Programming
Pouria Hajikarimi; Mehrdad Ehsani;
Fereidoon Moghadas Nejad; and
Amir H. Gandomi, A.M.ASCE
Published online: August 17, 2023
General Multifidelity Surrogate Models: Framework and Active-Learning Strategies for Efficient Rare Event Simulation
Promit Chakroborty; Somayajulu L. N. Dhulipala;
Yifeng Che;
Wen Jiang;
Benjamin W. Spencer;
Jason D. Hales; and
Michael D. Shields, M.ASCE
Published online: September 22, 2023
Embedding Prior Knowledge into Data-Driven Structural Performance Prediction to Extrapolate from Training Domains
Shi-Zhi Chen, M.ASCE; Shu-Ying Zhang;
De-Cheng Feng, M.ASCE; and
Ertugrul Taciroglu, M.ASCE
Published online: October 3, 2023