Deep learning architectures for data-driven damage detection in nonlinear dynamic systems

• URL: http://arxiv.org/abs/2407.03700v1
• Date: Thu, 4 Jul 2024 07:40:02 GMT
• Title: Deep learning architectures for data-driven damage detection in nonlinear dynamic systems
• Authors: Harrish Joseph, Giuseppe Quaranta, Biagio Carboni, Walter Lacarbonara,
• Abstract summary: In-depth investigation in the present work addresses deep learning applied to data-driven damage detection in nonlinear dynamic systems. Autoencoders (AEs) and generative adversarial networks (GANs) are implemented leveraging on 1D convolutional neural networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The primary goal of structural health monitoring is to detect damage at its onset before it reaches a critical level. The in-depth investigation in the present work addresses deep learning applied to data-driven damage detection in nonlinear dynamic systems. In particular, autoencoders (AEs) and generative adversarial networks (GANs) are implemented leveraging on 1D convolutional neural networks. The onset of damage is detected in the investigated nonlinear dynamic systems by exciting random vibrations of varying intensity, without prior knowledge of the system or the excitation and in unsupervised manner. The comprehensive numerical study is conducted on dynamic systems exhibiting different types of nonlinear behavior. An experimental application related to a magneto-elastic nonlinear system is also presented to corroborate the conclusions.

Related papers

• Reconstructing dynamics from sparse observations with no training on target system [0.0]
  The power of the proposed hybrid machine-learning framework is demonstrated using a large number of prototypical nonlinear dynamical systems. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the extreme situation where training data does not exist and the observations are random and sparse.
  arXiv  Detail & Related papers  (2024-10-28T17:05:04Z)
• Discovering Governing equations from Graph-Structured Data by Sparse Identification of Nonlinear Dynamical Systems [0.27624021966289597]
  We develop a new method called Sparse Identification of Dynamical Systems from Graph-structured data (SINDyG) SINDyG incorporates the network structure into sparse regression to identify model parameters that explain the underlying network dynamics.
  arXiv  Detail & Related papers  (2024-09-02T17:51:37Z)
• Learning System Dynamics without Forgetting [60.08612207170659]
  Predicting trajectories of systems with unknown dynamics is crucial in various research fields, including physics and biology. We present a novel framework of Mode-switching Graph ODE (MS-GODE), which can continually learn varying dynamics. We construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics.
  arXiv  Detail & Related papers  (2024-06-30T14:55:18Z)
• On instabilities in neural network-based physics simulators [0.0]
  Long-time dynamics produced by neural networks are often unphysical or unstable. We show that the rate of convergence of the training dynamics is uneven and depends on the distribution of energy in the data. Injecting synthetic noise into the data during training adds damping to the training dynamics and can stabilize the learned simulator.
  arXiv  Detail & Related papers  (2024-06-18T23:25:14Z)
• Learning Fine Scale Dynamics from Coarse Observations via Inner Recurrence [0.0]
  Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs) This paper presents a computational technique to learn the fine-scale dynamics from such coarsely observed data.
  arXiv  Detail & Related papers  (2022-06-03T20:28:52Z)
• Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations [68.62843292346813]
  We propose a structured latent ODE model that captures system input variations within its latent representation. Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
  arXiv  Detail & Related papers  (2022-02-25T20:00:56Z)
• Coupled and Uncoupled Dynamic Mode Decomposition in Multi-Compartmental Systems with Applications to Epidemiological and Additive Manufacturing Problems [58.720142291102135]
  We show that Dynamic Decomposition (DMD) may be a powerful tool when applied to nonlinear problems. In particular, we show interesting numerical applications to a continuous delayed-SIRD model for Covid-19.
  arXiv  Detail & Related papers  (2021-10-12T21:42:14Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Supervised DKRC with Images for Offline System Identification [77.34726150561087]
  Modern dynamical systems are becoming increasingly non-linear and complex. There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. Our approach learns these basis functions using a supervised learning approach.
  arXiv  Detail & Related papers  (2021-09-06T04:39:06Z)
• DynNet: Physics-based neural architecture design for linear and nonlinear structural response modeling and prediction [2.572404739180802]
  In this study, a physics-based recurrent neural network model is designed that is able to learn the dynamics of linear and nonlinear multiple degrees of freedom systems. The model is able to estimate a complete set of responses, including displacement, velocity, acceleration, and internal forces.
  arXiv  Detail & Related papers  (2020-07-03T17:05:35Z)
• Active Learning for Nonlinear System Identification with Guarantees [102.43355665393067]
  We study a class of nonlinear dynamical systems whose state transitions depend linearly on a known feature embedding of state-action pairs. We propose an active learning approach that achieves this by repeating three steps: trajectory planning, trajectory tracking, and re-estimation of the system from all available data. We show that our method estimates nonlinear dynamical systems at a parametric rate, similar to the statistical rate of standard linear regression.
  arXiv  Detail & Related papers  (2020-06-18T04:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.