Publications (12)
Sampling and active learning methods for network reliability estimation using K-terminal spanning tree
Chen Ding, Pengfei Wei, Yan Shi +3
Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method fo…
Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
Wang-Ji Yan, Lin-Feng Mei, Jiang Mo +3
In the era of big data, machine learning (ML) has become a powerful tool in various fields, notably impacting structural dynamics. ML algorithms offer advantages by modeling physic…
Interval propagation through the discrete Fourier transform
Marco De Angelis, Marco Behrendt, Liam Comerford +2
We present an algorithm for the forward propagation of intervals through the discrete Fourier transform. The algorithm yields best-possible bounds when computing the amplitude of t…
Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends
Wang-Ji Yan, Lin-Feng Mei, Yuan-Wei Yin +4
Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis…
Equation-Free Digital Twins for Nonlinear Structural Dynamics
Mohammad Mahdi Abaei, Ahmad BahooToroody, Arttu Polojärvi +4
Monitoring high-dimensional engineering structures in extreme environments is limited by non-stationary excitation, nonlinear structural kinematics, and stochastic forcing. Traditi…
Upper Approximation Bounds for Neural Oscillators
Zifeng Huang, Konstantin M. Zuev, Yong Xia +1
Neural oscillators, originating from second-order ordinary differential equations (ODEs), have demonstrated strong performance in stably learning causal mappings between long-term…
First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approach
Chen Ding, Chao Dang, Marcos A. Valdebenito +3
First-passage probability estimation of high-dimensional nonlinear stochastic systems is a significant task to be solved in many science and engineering fields, but remains still a…
Reliability of Critical Infrastructure Networks: Challenges
Konstantin Zuev, Michael Beer
Critical infrastructures form a technological skeleton of our world by providing us with water, food, electricity, gas, transportation, communication, banking, and finance. Moreove…
Transport Map Coupling Filter for State-Parameter Estimation
Jan Grashorn, Matteo Broggi, Ludovic Chamoin +1
Many dynamical systems are subjected to stochastic influences, such as random excitations, noise, and unmodeled behavior. Tracking the system's state and parameters based on a phys…
Upper Generalization Bounds for Neural Oscillators
Zifeng Huang, Konstantin M. Zuev, Yong Xia +1
Neural oscillators that originate from second-order ordinary differential equations (ODEs) have shown competitive performance in learning mappings between dynamic loads and respons…
Bayesian Updating of constitutive parameters under hybrid uncertainties with a novel surrogate model applied to biofilms
Lukas Fritsch, Hendrik Geisler, Jan Grashorn +5
Accurate modeling of bacterial biofilm growth is essential for understanding their complex dynamics in biomedical, environmental, and industrial settings. These dynamics are shaped…
Unified Framework for Hybrid Aleatory and Epistemic Uncertainty Propagation via Decoupled Multi-Probability Density Evolution Method
Yi Luo, Meng-Ze Lyu, Matteo Broggi +3
This paper presents a unified framework for uncertainty propagation in dynamical systems involving hybrid aleatory and epistemic uncertainties. The framework accommodates precise p…