Code:

Varitional autoencoder example applied to a beam case study

Dataset:

Synthetic dataset

Presentation by Jan: https://drive.google.com/file/d/1NNT02daIRjnIWiUI-gtx3bzJfk63z3nI/view

Fundamentals of the work presented in:

Koune I., Cicirello A., Disentangled representation learning with physics-informed variational autoencoder for structural health monitoring. EWSHM 2024

Code:

PhI-SINDY implementation for tackling non-smooth nonlinearities identification in SDOF, MDOF, multiple friction contacts.

Dataset:

Both synthetic and experimental dataset involving friction contacts

Paper:

Lathourakis C., Cicirello, A., Physics Enhanced Sparse Identification of Dynamical Systems with Discontinuous Nonlinearities.Nonlinear Dynamics, 2024.

https://doi.org/10.1007/s11071-024-09652-2

Code:

Switching Gaussian Process Latent Force model implementation for tackling non-smooth nonlinearity identification in a SDOF with one friction contact

Dataset:

Both synthetic and experimental dataset involving friction contacts

Paper:

Marino L., Cicirello A., A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearity, Data-Centric Engineering, 2023.

Open access: https://doi.org/10.1017/dce.2023.12 

Code:

Gaussian Problem Latent Force model implementation for virtual sensing applications

Dataset:

Synthetic dataset  (MDOF cantilever)

Paper:

Zou J., Lourens E., Cicirello A., Virtual sensing of subsoil strain response in monopile-based offshore wind turbines via Gaussian process latent force models,  Mechanical System and Signal Processing, 2023.

https://doi.org/10.1016/j.ymssp.2023.110488 

Code:

Efficient probabilistic model updating when dealing with spatial and temporal correlation

Dataset:

Synthetic dataset 

Paper:

Koune I., Rozsas I.,  Slobbe A.,  Cicirello A., Bayesian system identification for structures considering spatial and temporal correlation. Data Centric Engineering, 2023.

https://doi.org/10.1017/dce.2023.18

Code:

Efficient probabilistic model updating when dealing inference of time-varying model parameters - Sequential Ensemble Markov Chain Monte Carlo implementation 

Dataset:

Synthetic and experimental dataset for time-varying friction identification problem 

Paper:

Lye A., Marino L., Cicirello, A, Patelli E., Sequential Ensemble Monte Carlo sampler for on-line Bayesian inference of time-varying model parameters in engineering applications, ASME J. Risk Uncertainty Part B, 2023.

https://doi.org/10.1115/1.4056934 

Code:

Efficient probabilistic model updating - Transitional Ensemble Markov Chain Monte Carlo implementation 

Dataset:

Synthetic and experimental dataset  (Aluminum frame problem)

Paper:

Lye A., Cicirello A., Patelli E., An efficient and robust sampler for Bayesian inference: Transitional Ensemble Markov Chain Monte Carlo,  Mechanical System and Signal Processing, 2022

https://doi.org/10.1016/j.ymssp.2021.108471 

Code:

Codes for tutorial paper on Bayesian inference 

Dataset:

Synthetic dataset  

Paper:

Lye A., Cicirello A., Patelli E., Sampling Methods for solving Bayesian Model Updating Problems: A Tutorial, Mechanical System and Signal Processing, 2021.

https://doi.org/10.1016/j.ymssp.2021.107760