Physics-informed machine learning for Structural Health Monitoring

Physics-informed machine learning for Structural Health Monitoring

30 June 2022

D. J. Pitchforth

Papers citing "Physics-informed machine learning for Structural Health Monitoring"

9 / 9 papers shown

Title
Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations Alice Cicirello PINN AI4CE 42 9 0 08 May 2024
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications M. Haywood-Alexander Wei Liu Kiran Bacsa Zhilu Lai Eleni Chatzi AI4CE 13 9 0 31 Oct 2023
Towards a population-informed approach to the definition of data-driven models for structural dynamics G. Tsialiamanis N. Dervilis D. Wagg K. Worden AI4CE 34 5 0 19 Jul 2023
Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimization G. Drakoulas T. Gortsas D. Polyzos 32 2 0 18 Jul 2023
Improving aircraft performance using machine learning: a review S. L. Clainche E. Ferrer Sam Gibson Elisabeth Cross A. Parente Ricardo Vinuesa AI4CE 28 93 0 20 Oct 2022
Constraining Gaussian processes for physics-informed acoustic emission mapping Matthew R. Jones T. Rogers E. Cross AI4CE 17 16 0 03 Jun 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Barbara Z Cunha C. Droz A. Zine Stéphane Foulard M. Ichchou AI4CE 32 84 0 13 Apr 2022
Modelling variability in vibration-based PBSHM via a generalised population form T. Dardeno L. Bull Robin S. Mills N. Dervilis Keith Worden 29 4 0 14 Mar 2022
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves M. Haywood-Alexander N. Dervilis Keith Worden E. Cross Robin S. Mills T. Rogers 30 17 0 05 Jan 2021