Zobrazeno 1 - 10
of 164
pro vyhledávání: '"Diab W"'
Autor:
Asha Viswanath, Diab W. Abueidda, Mohamad Modrek, Rashid K. Abu Al-Rub, Seid Koric, Kamran A. Khan
Publikováno v:
Frontiers in Mechanical Engineering, Vol 10 (2024)
Data-driven models that act as surrogates for computationally costly 3D topology optimization techniques are very popular because they help alleviate multiple time-consuming 3D finite element analyses during optimization. In this study, one such 3D C
Externí odkaz:
https://doaj.org/article/0961fc53c58e472baded1ea04e8b9d11
Autor:
Ahmed, Bilal, Qiu, Yuqing, Abueidda, Diab W., El-Sekelly, Waleed, de Soto, Borja Garcia, Abdoun, Tarek, Mobasher, Mostafa E.
Finite element modeling is a well-established tool for structural analysis, yet modeling complex structures often requires extensive pre-processing, significant analysis effort, and considerable time. This study addresses this challenge by introducin
Externí odkaz:
http://arxiv.org/abs/2409.00994
Damage identification for bridges using machine learning: Development and application to KW51 bridge
Autor:
Qiu, Yuqing, Ahmed, Bilal, Abueidda, Diab W., El-Sekelly, Waleed, de Soto, Borja Garcia, Abdoun, Tarek, Ji, Hongli, Qiu, Jinhao, Mobasher, Mostafa E.
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive machine learning based damage identification (CMLDI) method that integrate
Externí odkaz:
http://arxiv.org/abs/2408.03002
The modern digital engineering design often requires costly repeated simulations for different scenarios. The prediction capability of neural networks (NNs) makes them suitable surrogates for providing design insights. However, only a few NNs can eff
Externí odkaz:
http://arxiv.org/abs/2405.19143
Publikováno v:
Materials & Design, Vol 196, Iss , Pp 109098- (2020)
Data-driven models are rising as an auspicious method for the geometrical design of materials and structural systems. Nevertheless, existing data-driven models customarily address the optimization of structural designs rather than metamaterial design
Externí odkaz:
https://doaj.org/article/dc6d7cda7db148f298f45a39bd8da6d5
Autor:
Diab W. Abueidda, Mohamed Elhebeary, Cheng-Shen (Andrew) Shiang, Siyuan Pang, Rashid K. Abu Al-Rub, Iwona M. Jasiuk
Publikováno v:
Materials & Design, Vol 165, Iss , Pp - (2019)
Gyroid is a member of the triply periodic minimal surfaces (TPMS) family. In this paper, the mechanical properties of Gyroid-structures are investigated both experimentally and computationally. 3D printing is used to fabricate polymeric Gyroid-struct
Externí odkaz:
https://doaj.org/article/f22dfb13e7514b1dbec31199d8c2b436
Autor:
Seid Koric, Diab W. Abueidda
Publikováno v:
Metals, Vol 11, Iss 3, p 494 (2021)
The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challen
Externí odkaz:
https://doaj.org/article/c574991f1445434ca766fea651c296dc
Most currently available methods for modeling multiphysics, including thermoelasticity, using machine learning approaches, are focused on solving complete multiphysics problems using data-driven or physics-informed multi-layer perceptron (MLP) networ
Externí odkaz:
http://arxiv.org/abs/2305.17799
Autor:
Viswanath, Asha, Abueidda, Diab W, Modrek, Mohamad, Khan, Kamran A, Koric, Seid, Al-Rub, Rashid K. Abu
Triply periodic minimal surface (TPMS) metamaterials characterized by mathematically-controlled topologies exhibit better mechanical properties compared to uniform structures. The unit cell topology of such metamaterials can be further optimized to i
Externí odkaz:
http://arxiv.org/abs/2303.10007
Physics-informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, physics-informed neural network models suffer from several issues and ca
Externí odkaz:
http://arxiv.org/abs/2205.14148