Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
SA Faroughi, N Pawar, C Fernandes, M Raissi… - ar** a systematic knowledge trend for building energy consumption prediction
The rapid depletion of natural sources of energy, coupled with increasing global population
has triggered the emergence of various techniques and strategies for building energy …
has triggered the emergence of various techniques and strategies for building energy …
Damage identification of steel bridge based on data augmentation and adaptive optimization neural network
M Huang, J Zhang, J Li, Z Deng… - Structural Health …, 2024 - journals.sagepub.com
With the advancement of deep learning, data-driven structural damage identification (SDI)
has shown considerable development. However, collecting vibration signals related to …
has shown considerable development. However, collecting vibration signals related to …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure
Abstract Structural Health Monitoring (SHM) is an emerging field of engineering with a wide
range of applications. The most common SHM strategies operate on structural responses …
range of applications. The most common SHM strategies operate on structural responses …
A novel deep unsupervised learning-based framework for optimization of truss structures
In this paper, an efficient deep unsupervised learning (DUL)-based framework is proposed
to directly perform the design optimization of truss structures under multiple constraints for …
to directly perform the design optimization of truss structures under multiple constraints for …
A machine learning-based surrogate model for optimization of truss structures with geometrically nonlinear behavior
Abstract Design optimization of geometrically nonlinear structures is well known as a
computationally expensive problem by using incremental-iterative solution techniques. To …
computationally expensive problem by using incremental-iterative solution techniques. To …
Deep learning for intelligent prediction of rock strength by adopting measurement while drilling data
Precise, rapid, and reliable prediction of rock strength parameters is of great significance for
underground engineering. This paper presents a method for predicting rock strength …
underground engineering. This paper presents a method for predicting rock strength …
A deep neural network-assisted metamodel for damage detection of trusses using incomplete time-series acceleration
QX Lieu - Expert Systems with Applications, 2023 - Elsevier
In this article, a deep neural network (DNN)-driven metamodel is first introduced to damage
identification of trusses utilizing acceleration signals incompletely measured from limited …
identification of trusses utilizing acceleration signals incompletely measured from limited …
One-dimensional convolutional neural network for damage detection of jacket-type offshore platforms
X Bao, T Fan, C Shi, G Yang - Ocean Engineering, 2021 - Elsevier
Vibration-based damage detection techniques play an important role in health monitoring of
offshore structures. This study explores the possibility to use the one-dimensional …
offshore structures. This study explores the possibility to use the one-dimensional …