Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 129119 |
| Seitenumfang | 15 |
| Fachzeitschrift | NEUROCOMPUTING |
| Jahrgang | 619 |
| Frühes Online-Datum | 7 Dez. 2024 |
| Publikationsstatus | Veröffentlicht - 28 Feb. 2025 |
Abstract
This study help investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework. The beam material properties are assumed to vary as an arbitrary continuous function. The methods consider the output of a neural network/operator as an approximation to the displacement fields and derive the equations governing beam behavior based on the continuum formulation. They are implemented in the framework and formulated by three approaches: (a) the vector approach leads to a Physics-Informed Neural Network (PINN), (b) the energy approach brings about the Deep Energy Method (DEM), and (c) the data-driven approach, which results in a class of Neural Operator methods. Finally, a neural operator has been trained to predict the response of the porous beam with functionally graded material under any porosity distribution pattern and any arbitrary traction condition. The results are validated with analytical and numerical reference solutions. The data and code accompanying this manuscript will be publicly available at https://github.com/eshaghi-ms/DeepNetBeam.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Neurowissenschaften (insg.)
- Kognitive Neurowissenschaft
- Informatik (insg.)
- Artificial intelligence
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in: NEUROCOMPUTING, Jahrgang 619, 129119, 28.02.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Applications of scientific machine learning for the analysis of functionally graded porous beams
AU - Eshaghi, Mohammad Sadegh
AU - Bamdad, Mostafa
AU - Anitescu, Cosmin
AU - Wang, Yizheng
AU - Zhuang, Xiaoying
AU - Rabczuk, Timon
N1 - Publisher Copyright: © 2024 The Authors
PY - 2025/2/28
Y1 - 2025/2/28
N2 - This study help investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework. The beam material properties are assumed to vary as an arbitrary continuous function. The methods consider the output of a neural network/operator as an approximation to the displacement fields and derive the equations governing beam behavior based on the continuum formulation. They are implemented in the framework and formulated by three approaches: (a) the vector approach leads to a Physics-Informed Neural Network (PINN), (b) the energy approach brings about the Deep Energy Method (DEM), and (c) the data-driven approach, which results in a class of Neural Operator methods. Finally, a neural operator has been trained to predict the response of the porous beam with functionally graded material under any porosity distribution pattern and any arbitrary traction condition. The results are validated with analytical and numerical reference solutions. The data and code accompanying this manuscript will be publicly available at https://github.com/eshaghi-ms/DeepNetBeam.
AB - This study help investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework. The beam material properties are assumed to vary as an arbitrary continuous function. The methods consider the output of a neural network/operator as an approximation to the displacement fields and derive the equations governing beam behavior based on the continuum formulation. They are implemented in the framework and formulated by three approaches: (a) the vector approach leads to a Physics-Informed Neural Network (PINN), (b) the energy approach brings about the Deep Energy Method (DEM), and (c) the data-driven approach, which results in a class of Neural Operator methods. Finally, a neural operator has been trained to predict the response of the porous beam with functionally graded material under any porosity distribution pattern and any arbitrary traction condition. The results are validated with analytical and numerical reference solutions. The data and code accompanying this manuscript will be publicly available at https://github.com/eshaghi-ms/DeepNetBeam.
KW - Deep energy method
KW - Fourier neural operator
KW - Functionally graded material
KW - Physics-informed neural network
KW - Porous beam
KW - Scientific machine learning
UR - http://www.scopus.com/inward/record.url?scp=85212570136&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2408.02698
DO - 10.48550/arXiv.2408.02698
M3 - Article
AN - SCOPUS:85212570136
VL - 619
JO - NEUROCOMPUTING
JF - NEUROCOMPUTING
SN - 0925-2312
M1 - 129119
ER -