A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials

D Bishara, Y Xie, WK Liu, S Li - Archives of computational methods in …, 2023 - Springer
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …

Real‐time biomechanics using the finite element method and machine learning: Review and perspective

R Phellan, B Hachem, J Clin, JM Mac‐Thiong… - Medical …, 2021 - Wiley Online Library
Purpose The finite element method (FEM) is the preferred method to simulate phenomena in
anatomical structures. However, purely FEM‐based mechanical simulations require …

Stressgan: A generative deep learning model for two-dimensional stress distribution prediction

H Jiang, Z Nie, R Yeo… - Journal of Applied …, 2021 - asmedigitalcollection.asme.org
Using deep learning to analyze mechanical stress distributions is gaining interest with the
demand for fast stress analysis. Deep learning approaches have achieved excellent …

A machine learning approach as a surrogate for a finite element analysis: Status of research and application to one dimensional systems

P Vurtur Badarinath, M Chierichetti, F Davoudi Kakhki - Sensors, 2021 - mdpi.com
Current maintenance intervals of mechanical systems are scheduled a priori based on the
life of the system, resulting in expensive maintenance scheduling, and often undermining …

Bridging the gap between mechanistic biological models and machine learning surrogates

IM Gherman, ZS Abdallah, W Pang… - PLoS Computational …, 2023 - journals.plos.org
Mechanistic models have been used for centuries to describe complex interconnected
processes, including biological ones. As the scope of these models has widened, so have …

Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components

H Bolandi, X Li, T Salem, VN Boddeti… - Frontiers of Structural and …, 2022 - Springer
Finite-element analysis (FEA) for structures has been broadly used to conduct stress
analysis of various civil and mechanical engineering structures. Conventional methods …

Deep learning-based surrogate model for three-dimensional patient-specific computational fluid dynamics

P Du, X Zhu, JX Wang - Physics of Fluids, 2022 - pubs.aip.org
Optimization and uncertainty quantification have been playing an increasingly important role
in computational hemodynamics. However, existing methods based on principled modeling …

Simulation of postoperative facial appearances via geometric deep learning for efficient orthognathic surgical planning

L Ma, D Xiao, D Kim, C Lian, T Kuang… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Orthognathic surgery corrects jaw deformities to improve aesthetics and functions. Due to
the complexity of the craniomaxillofacial (CMF) anatomy, orthognathic surgery requires …

Application of machine learning and deep learning in finite element analysis: a comprehensive review

D Nath, Ankit, DR Neog, SS Gautam - Archives of computational methods …, 2024 - Springer
Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …

[HTML][HTML] Development of numerical model-based machine learning algorithms for different healing stages of distal radius fracture healing

X Liu, S Miramini, M Patel, P Ebeling, J Liao… - Computer Methods and …, 2023 - Elsevier
Background and objectives Early therapeutic exercises are vital for the healing of distal
radius fractures (DRFs) treated with the volar locking plate. However, current development of …