Core Research
Our research is curiosity-driven with interest in solving fundamental scientific questions and in tackling pressing scientific challenges in Engineering for guiding decision making on complex engineering systems, critical structures, and important functional components.
Collaborations with industries
Much of the fundamental research that has been carried out within the DVU group was stemming from challenges identified with industrial collaborators. The projects carried out have resulted in new methodologies, (mostly open access) tools, publications, and effective knowledge transfer to our industrial partners.
We have experience working with a broad range of industrial partners, including energy, construction, aerospace, monitoring and sensing, and defense sectors.
We do not have schemes to carry out research through undergraduate or MEng research projects.
If you are interested in sponsoring research assistants/associates and/or PhD students, or putting together a joint research proposal, please do get in touch directly with Dr Cicirello at ac685@cam.ac.uk
Dr Cicirello is also available for technical consultancy and for technical training sessions.
2023 research outputs review of the DVU group
2023_DVU_research_outputs.pdf
Our research strategy is based on developing and integrating state-of-the-art physics-based models, laboratory experiments, monitoring, data-driven techniques, physics-informed machine learning, and uncertainty quantification approaches.
Research interests: investigation of the dynamic performance of complex engineering systems, critical structures, and important functional components, when subjected to manufacturing variability, uncertainty and nonlinearity.
Research aim: developing techniques for guiding decision making on such systems at the design-stage and in operating conditions to avoid unexpected failures and/or performance issues.
The DVU group's research is structured into four research themes:
Theme 1: Learning from data, models and knowledge: Physics-Enhanced Machine Learning
Focus:
Development of techniques for the development of digital twins to assess the remaining life of structures and explore failure prevention under uncertainty, sparse and gappy data and nonlinearity.
Development of techniques for system identification, structural health monitoring and maintenance strategies,
Ongoing research:; Physics-enhanced Machine Learning; Nonlinear System identification under nonsmooth nonlinearity; Structural Health Monitoring; Reinforcement Learning for Maintenance Scheduling; Virtual sensing and sensor placement;
Key publications:
1) Cicirello A., Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations, Under review, 2024.
preprint: https://arxiv.org/abs/2405.05987
2) 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
Codes and experimental dataset available: https://github.com/xristosl0610/PhI-SINDy
3) 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.
4) Koune I., Rozsas I., Slobbe A., Cicirello A., Bayesian system identification for structures considering spatial and temporal correlation. Data Centric Engineering, 2023.
Theme 2: Uncertainty Quantification & Manufacturing variability
Focus:
Development of approaches for inverse uncertainty quantification (quantify uncertainty in the input parameters using measurements) for expensive-to-evaluate computational model, multimodal posteriors, expensive-to-evaluate likelihood
Development of approaches for modelling uncertainty in the input parameter under limited information;
Development of approaches for efficiently propagating uncertainty (forward problem) in order to quantify their effects on the reliability and performance of a structure at the design stage.
Ongoing research: Efficient sampling techniques, Variational inference, Propagation of intervals; propagation of mixed types of uncertainty descriptions; Imprecise probability models; incorporation of uncertainty and variability within wave-based approaches such as the Wave Finite Element Method; Statistical Energy Analysis; Hybrid Finite Element/Statistical Energy Analysis.
Key publications:
1) 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.
2) Igea F., Cicirello A., Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation, Mechanical System and Signal Processing, 2023
3) Cicirello A., Giunta F., Machine Learning based optimization for interval uncertainty propagation. Mechanical System and Signal Processing, 2022.
Theme 3: Information extraction from heterogeneous and sparse sources: signal processing & Artificial Intelligence
Focus:
Development of techniques for extracting robust features and information from measurements, images, monitoring campaigns and expert opinions.
Development of techniques for extracting labels, graphs and information from documentation and failure investigations.
Development of techniques for extracting governing equations from data
Ongoing research: Predicting failures of the monitoring systems using Machine Learning and Natural Language Processing techniques; distinction of confounding influences; Automatic generation of systems modelling language diagrams; Extracting governing equations from measurements only.
Key publications:
1) Mahajan S., Cicirello A, Governing Equation identification of nonlinear single degree of freedom oscillators with Coulomb friction using explicit stick and slip temporal constraint, ASME J. Risk Uncertainty Part B, 2023.
2) Zhong S., Scarinci A., Cicirello A., Natural Language Processing for Systems Engineering: Automatic Generation of Systems Modelling Language Diagrams, Knowledge-Based Systems, 2023.
3) Oncescu A-M., Cicirello A., A self-supervised classification algorithm for sensor fault identification for robust Structural Health Monitoring, 10th European Workshop on Structural Health Monitoring (EWSHM 2022), Palermo, Italy, 2022.
Theme 4: Laboratory experiments and advanced physics-based models
Focus:
Development of laboratory experiments for efficiently characterizing manufacturing variability and non-linearity; to investigate the response metrics obtained with advanced models, to validate and verify advanced physics-based models, and to assess effective monitoring strategies.
Development of advanced (analytical and numerical) linear and non-linear physics-based models for assessing the dynamic and vibro-acoustic performance of structural components and complex built-up structures.
Ongoing research: Friction; Constrained Layer Damping; Vibro-acoustic non-destructive testing for manufacturing variability characterization; friction metrics assessments; validation of friction models; effective sensor location placement.
Key publications:
1) Cabboi, A., Marino, L., Cicirello, A., A comparative study between Amontons-Coulomb and Dieterich-Ruina friction laws for the cyclic response of a single degree of freedom system, European Journal of Mechanics A - Solids, 2022. Paper available here
2) Marino, Cicirello, Experimental investigation of a single-degree-of-freedom system with Coulomb friction, Nonlinear Dynamics, 2020. Paper available here
3) Igea, Cicirello, Part-to-part variability assessment of material properties for flat thin orthotropic rectangular panels using Chladni patterns, Mechanical System and Signal Processing, 2020. Paper available here
Graphical overview of some of the research questions being tackled within the DVU group
