Dr Eky Febrianto

  • Lecturer in Computational Mechanics (Infrastructure & Environment)

email: Eky.Febrianto@glasgow.ac.uk

518 Rankine Building, University of Glasgow, Scotland, United Kingdom, G12 8LT

Import to contacts

ORCID iDhttps://orcid.org/0000-0001-8838-6240

Biography

Eky joined the James Watt School of Engineering as Lecturer in Computational Mechanics. He obtained a Bachelor and Masters degree (Cum Laude) from the Bandung Institute of Technology, Indonesia. He completed his PhD at University of Cambridge in 2020 and was a research associate at The Alan Turing Institute. He was a visiting research fellow at the University of Cambridge in 2022 and the Bandung Institute of Technology. He has experience in developing physics-informed digital twins of infrastructures by incorporating numerical analysis and structural health monitoring data. He has also worked on innovative numerical methods that enables robust mesh-free analysis of structures with complex geometries.

Research interests

My research interests include Bayesian methods for physics-inform digital twinning of complex engineering systems, inverse problem with application to material property identification from data, and computational mechanics including isogeometric analysis and mesh-free methods.

Publications

List by: Type | Date

Jump to: 2023
Number of items: 1.

2023

Smith, M. G., Radford, J. , Febrianto, E. , Ramírez, J., O'Mahony, H., Matheson, A. B., Gibson, G. M. , Faccio, D. and Tassieri, M. (2023) Machine learning opens a doorway for microrheology with optical tweezers in living systems. AIP Advances, 13(7), 075315. (doi: 10.1063/5.0161014)

This list was generated on Thu Feb 22 00:10:04 2024 GMT.
Jump to: Articles
Number of items: 1.

Articles

Smith, M. G., Radford, J. , Febrianto, E. , Ramírez, J., O'Mahony, H., Matheson, A. B., Gibson, G. M. , Faccio, D. and Tassieri, M. (2023) Machine learning opens a doorway for microrheology with optical tweezers in living systems. AIP Advances, 13(7), 075315. (doi: 10.1063/5.0161014)

This list was generated on Thu Feb 22 00:10:04 2024 GMT.

Grants

  • Royal Society Research Grant (2022-2023): Robust digital twinning of complex structures using implicit geometry

 

Supervision

If you are interested in the research topics above, please contact me via email for PhD opportunities.

 

  • Chen, Qianxu
    Physics-constrained data-driven modelling of anisotropic sand behaviour
  • Sungurtekin, Turkay
    Data-driven modelling of sand behaviour under cyclic loading
  • Zheng, Timo
    Smart Characterisation of Offshore Geo-materials Using Database Methods

Teaching

  • Fluid Mechanics 2