A survey of machine learning techniques in structural and multidisciplinary optimization
Abstract Machine Learning (ML) techniques have been used in an extensive range of
applications in the field of structural and multidisciplinary optimization over the last few …
applications in the field of structural and multidisciplinary optimization over the last few …
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 …
A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic–vibration interaction problems
We propose an efficient Monte Carlo simulation method to address the multivariate
uncertainties in acoustic–vibration interaction systems. The deep neural network acts as a …
uncertainties in acoustic–vibration interaction systems. The deep neural network acts as a …
A machine learning framework for accelerating the design process using CAE simulations: An application to finite element analysis in structural crashworthiness
CP Kohar, L Greve, TK Eller, DS Connolly… - Computer Methods in …, 2021 - Elsevier
This paper presents a novel framework for predicting computer-aided engineering (CAE)
simulation results using machine learning (ML). The framework is applied to finite element …
simulation results using machine learning (ML). The framework is applied to finite element …
Enhancing deep neural networks for multivariate uncertainty analysis of cracked structures by POD-RBF
X Shen, C Du, S Jiang, L Sun, L Chen - Theoretical and Applied Fracture …, 2023 - Elsevier
Abstract An efficient Monte Carlo (MC) simulation method is proposed to address
multivariate uncertainties in the dynamic fracture analysis of cracked structures. Deep neural …
multivariate uncertainties in the dynamic fracture analysis of cracked structures. Deep neural …
Deep learning in computational mechanics: a review
L Herrmann, S Kollmannsberger - Computational Mechanics, 2024 - Springer
The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
Machine learning-based crashworthiness optimization for the square cone energy-absorbing structure of the subway vehicle
This paper presents a novel framework for predicting the crashworthiness of a square cone
energy-absorbing (SCEA) structure using a machine-learning method. The structure …
energy-absorbing (SCEA) structure using a machine-learning method. The structure …
Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps
G Solorzano, V Plevris - Frontiers in Built Environment, 2022 - frontiersin.org
The modeling and simulation of structural systems is a task that requires high precision and
reliable results to ensure the stability and safety of construction projects of all kinds. For …
reliable results to ensure the stability and safety of construction projects of all kinds. For …
Learning finite element convergence with the multi-fidelity graph neural network
Abstract Machine learning techniques have emerged as potential alternatives to traditional
physics-based modeling and partial differential equation solvers. Among these machine …
physics-based modeling and partial differential equation solvers. Among these machine …
Machine Learning in Computer Aided Engineering
The extraordinary success of Machine Learning (ML) in many complex heuristic fields has
promoted its introduction in more analytical engineering fields, improving or substituting …
promoted its introduction in more analytical engineering fields, improving or substituting …