Recent advances in uncertainty quantification in structural response characterization and system identification
Structural dynamics has numerous practical applications, such as structural analysis,
vibration control, energy harvesting, system identification, structural safety assessment, and …
vibration control, energy harvesting, system identification, structural safety assessment, and …
A comparative study of various metamodeling approaches in tunnel reliability analysis
Various metamodeling approaches are applied in conjunction with Monte Carlo simulation
and or the second moment-based method for reliability analyses of underground tunnels …
and or the second moment-based method for reliability analyses of underground tunnels …
A hybrid physics-informed machine learning approach for time-dependent reliability assessment of electromagnetic relays
F Mei, H Chen, W Yang, G Zhai - Reliability Engineering & System Safety, 2024 - Elsevier
Electromagnetic relays (EMRs) are intricate micro-electromechanical systems characterized
by nonlinear behavior and coupling effects between electromagnetic and mechanical forces …
by nonlinear behavior and coupling effects between electromagnetic and mechanical forces …
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes
Abstract Machine learning models trained with structural health monitoring data have
become a powerful tool for system identification. This paper presents a physics-informed …
become a powerful tool for system identification. This paper presents a physics-informed …
DPA-WNO: A gray box model for a class of stochastic mechanics problem
S Chakraborty - arXiv preprint arXiv:2309.15128, 2023 - arxiv.org
The well-known governing physics in science and engineering is often based on certain
assumptions and approximations. Therefore, analyses and designs carried out based on …
assumptions and approximations. Therefore, analyses and designs carried out based on …
Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective
The traditional methods for probabilistic analysis of physical systems often follow a non-
intrusive scheme with, random samples for stochastic model parameters generated in the …
intrusive scheme with, random samples for stochastic model parameters generated in the …
[HTML][HTML] A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis
Reliability analysis of highly sensitive structures is crucial to prevent catastrophic failures
and ensure safety. Therefore, these safety-critical systems are to be designed for extremely …
and ensure safety. Therefore, these safety-critical systems are to be designed for extremely …
Dimensional reduction technique-based maximum entropy principle method for safety degree analysis under twofold random uncertainty
K Feng, Z Lu, H Li, P He, Y Dai - Probabilistic Engineering Mechanics, 2024 - Elsevier
A modified failure chance measure (FCM) was proposed to assess the safety degree of
structures under the influence of twofold random uncertainty. This uncertainty arises from …
structures under the influence of twofold random uncertainty. This uncertainty arises from …
[PDF][PDF] Model-operator fusion for scientific machine learning
S Chakraborty - casml.cc
The governing physics in science and engineering is often based on assumptions and
approximations, leading to analyses and designs that are also approximate. While data …
approximations, leading to analyses and designs that are also approximate. While data …