Deep generative models in engineering design: A review

L Regenwetter, AH Nobari… - Journal of …, 2022 - asmedigitalcollection.asme.org
Automated design synthesis has the potential to revolutionize the modern engineering
design process and improve access to highly optimized and customized products across …

Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions

Z Wang, W Yang, Q Liu, Y Zhao, P Liu, D Wu… - Journal of Manufacturing …, 2022 - Elsevier
A thorough understanding of complex process-structure-property (PSP) relationships in
additive manufacturing (AM) has long been pursued due to its paramount importance in …

[HTML][HTML] Stress field prediction in fiber-reinforced composite materials using a deep learning approach

A Bhaduri, A Gupta, L Graham-Brady - Composites Part B: Engineering, 2022 - Elsevier
Stress analysis is an important step in the design of material systems, and finite element
methods (FEM) are a standard approach of performing computational analysis of stresses in …

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

M Mahmoudabadbozchelou, GE Karniadakis, S Jamali - Soft Matter, 2022 - pubs.rsc.org
Time-and rate-dependent material functions in non-Newtonian fluids in response to different
deformation fields pose a challenge in integrating different constitutive models into …

A framework based on physics-informed neural networks and extreme learning for the analysis of composite structures

CA Yan, R Vescovini, L Dozio - Computers & Structures, 2022 - Elsevier
This paper presents a novel approach for solving direct problems in linear elasticity
involving plate and shell structures. The method relies upon a combination of Physics …

Data-driven methods for stress field predictions in random heterogeneous materials

E Hoq, O Aljarrah, J Li, J Bi, A Heryudono… - … Applications of Artificial …, 2023 - Elsevier
Predicting full-field stress responses is of fundamental importance to assessing materials
failure and has various engineering applications in design optimization, manufacturing …

A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features

V Krokos, V Bui Xuan, SPA Bordas, P Young… - Computational …, 2022 - Springer
Multiscale computational modelling is challenging due to the high computational cost of
direct numerical simulation by finite elements. To address this issue, concurrent multiscale …

[HTML][HTML] StressD: 2D Stress estimation using denoising diffusion model

Y Jadhav, J Berthel, C Hu, R Panat, J Beuth… - Computer Methods in …, 2023 - Elsevier
Finite element analysis (FEA), a common approach for simulating stress distribution for a
given geometry, is generally associated with high computational cost, especially when high …

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 …

Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks

BP Croom, M Berkson, RK Mueller, M Presley… - Mechanics of …, 2022 - Elsevier
In context of the universal presence of defects in additively manufactured (AM) metals,
efficient computational tools are required to rapidly screen AM microstructures for …