Responses of stochastic dynamical systems by the generalized cell mapping method with deep learning
Experimental data is often corrupted by measurement noise in practical engineering and
there are multiple observed data under the same experimental condition. The noisy …
there are multiple observed data under the same experimental condition. The noisy …
Generalized cell mapping method with deep learning for global analysis and response prediction of dynamical systems
A novel method that combines generalized cell mapping and deep learning is developed to
analyze the global properties and predict the responses of dynamical systems. The …
analyze the global properties and predict the responses of dynamical systems. The …
Probabilistic response of dynamical systems based on the global attractor with the compatible cell mapping method
A generalized compatible cell mapping (CCM) method is proposed in this paper to take
advantages of the simple cell mapping (SCM) method, the generalized cell mapping (GCM) …
advantages of the simple cell mapping (SCM) method, the generalized cell mapping (GCM) …
On the data-driven generalized cell mapping method
Global analysis is often necessary for exploiting various applications or understanding the
mechanisms of many dynamical phenomena in engineering practice where the underlying …
mechanisms of many dynamical phenomena in engineering practice where the underlying …
Non-stationary response of MDOF dynamical systems under combined Gaussian and Poisson white noises by the generalized cell mapping method
A block matrix analysis procedure of the generalized cell mapping (GCM) method is
proposed in this paper. The proposed method solves the storage problem in the response …
proposed in this paper. The proposed method solves the storage problem in the response …
The generalized cell mapping method in nonlinear random vibration based upon short-time Gaussian approximation
JQ Sun, CS Hsu - 1990 - asmedigitalcollection.asme.org
A short-time Gaussian approximation scheme is proposed in the paper. This scheme
provides a very efficient and accurate way of computing the one-step transition probability …
provides a very efficient and accurate way of computing the one-step transition probability …
Failure diagnosis system using a new nonlinear mapping augmentation approach for deep learning algorithm
This paper develops a new nonlinear transformation-based augmentation method for
Convolutional Neural Network (CNN) approach with vibration signals of simple, small scale …
Convolutional Neural Network (CNN) approach with vibration signals of simple, small scale …
The dimension-reduction strategy via mapping for probability density evolution analysis of nonlinear stochastic systems
J Li, JB Chen - Probabilistic Engineering Mechanics, 2006 - Elsevier
Dynamic response analysis of nonlinear structures involving random parameters has for a
long time been an important and challenging problem. In recent years, the probability …
long time been an important and challenging problem. In recent years, the probability …
Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks
In the application of structural health monitoring, the measured data might be temporarily or
permanently lost due to sensor fault or transmission failure. The measured data with a high …
permanently lost due to sensor fault or transmission failure. The measured data with a high …
Path integration method based on a decoupling probability mapping for fast solving the stochastic response of dynamical systems
J Peng, L Wang, B Wang, S Dong, W Xu - International Journal of Non …, 2023 - Elsevier
An efficient path integration method is proposed for obtaining the stochastic response of
dynamical systems. This method benefits from a new short-time transition probability density …
dynamical systems. This method benefits from a new short-time transition probability density …