Deep generative models in engineering design: A review
Automated design synthesis has the potential to revolutionize the modern engineering
design process and improve access to highly optimized and customized products across …
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
A thorough understanding of complex process-structure-property (PSP) relationships in
additive manufacturing (AM) has long been pursued due to its paramount importance 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
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 …
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
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 …
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
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 …
involving plate and shell structures. The method relies upon a combination of Physics …
Data-driven methods for stress field predictions in random heterogeneous materials
Predicting full-field stress responses is of fundamental importance to assessing materials
failure and has various engineering applications in design optimization, manufacturing …
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
Multiscale computational modelling is challenging due to the high computational cost of
direct numerical simulation by finite elements. To address this issue, concurrent multiscale …
direct numerical simulation by finite elements. To address this issue, concurrent multiscale …
[HTML][HTML] StressD: 2D Stress estimation using denoising diffusion model
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 …
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
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 …
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
In context of the universal presence of defects in additively manufactured (AM) metals,
efficient computational tools are required to rapidly screen AM microstructures for …
efficient computational tools are required to rapidly screen AM microstructures for …