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: 2024 | 2023 | 2022 | 2021
Number of items: 8.

2024

Febrianto, E. , Šístek, J., Kůs, P., Kecman, M. and Cirak, F. (2024) A three-grid high-order immersed finite element method for the analysis of CAD models. Computer-Aided Design, 173, 103730. (doi: 10.1016/j.cad.2024.103730)

Alfarisy, D. , Zuhal, L., Ortiz, M., Cirak, F. and Febrianto, E. (2024) Point collocation with mollified piecewise polynomial approximants for high-order partial differential equations. International Journal for Numerical Methods in Engineering, (doi: 10.1002/nme.7548) (Early Online Publication)

2023

Sun, F., Febrianto, E. , Fernando, H., Butler, L. J., Cirak, F. and Hoult, N. A. (2023) Data-informed statistical finite element analysis of rail buckling. Computers and Structures, 289, 107163. (doi: 10.1016/j.compstruc.2023.107163)

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)

2022

Febrianto, E. , Butler, L., Girolami, M. and Cirak, F. (2022) Digital twinning of self-sensing structures using the statistical finite element method. Data-Centric Engineering, 3, e31. (doi: 10.1017/dce.2022.28)

Povala, J., Kazlauskaite, I., Febrianto, E. , Cirak, F. and Girolami, M. (2022) Variational Bayesian approximation of inverse problems using sparse precision matrices. Computer Methods in Applied Mechanics and Engineering, 393, 114712. (doi: 10.1016/j.cma.2022.114712)

2021

Girolami, M., Febrianto, E. , Yin, G. and Cirak, F. (2021) The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions. Computer Methods in Applied Mechanics and Engineering, 375, 113533. (doi: 10.1016/j.cma.2020.113533)

Febrianto, E. , Ortiz, M. and Cirak, F. (2021) Mollified finite element approximants of arbitrary order and smoothness. Computer Methods in Applied Mechanics and Engineering, 373, 113513. (doi: 10.1016/j.cma.2020.113513)

This list was generated on Mon Jun 17 18:57:52 2024 BST.
Jump to: Articles
Number of items: 8.

Articles

Febrianto, E. , Šístek, J., Kůs, P., Kecman, M. and Cirak, F. (2024) A three-grid high-order immersed finite element method for the analysis of CAD models. Computer-Aided Design, 173, 103730. (doi: 10.1016/j.cad.2024.103730)

Alfarisy, D. , Zuhal, L., Ortiz, M., Cirak, F. and Febrianto, E. (2024) Point collocation with mollified piecewise polynomial approximants for high-order partial differential equations. International Journal for Numerical Methods in Engineering, (doi: 10.1002/nme.7548) (Early Online Publication)

Sun, F., Febrianto, E. , Fernando, H., Butler, L. J., Cirak, F. and Hoult, N. A. (2023) Data-informed statistical finite element analysis of rail buckling. Computers and Structures, 289, 107163. (doi: 10.1016/j.compstruc.2023.107163)

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)

Febrianto, E. , Butler, L., Girolami, M. and Cirak, F. (2022) Digital twinning of self-sensing structures using the statistical finite element method. Data-Centric Engineering, 3, e31. (doi: 10.1017/dce.2022.28)

Povala, J., Kazlauskaite, I., Febrianto, E. , Cirak, F. and Girolami, M. (2022) Variational Bayesian approximation of inverse problems using sparse precision matrices. Computer Methods in Applied Mechanics and Engineering, 393, 114712. (doi: 10.1016/j.cma.2022.114712)

Girolami, M., Febrianto, E. , Yin, G. and Cirak, F. (2021) The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions. Computer Methods in Applied Mechanics and Engineering, 375, 113533. (doi: 10.1016/j.cma.2020.113533)

Febrianto, E. , Ortiz, M. and Cirak, F. (2021) Mollified finite element approximants of arbitrary order and smoothness. Computer Methods in Applied Mechanics and Engineering, 373, 113513. (doi: 10.1016/j.cma.2020.113513)

This list was generated on Mon Jun 17 18:57:52 2024 BST.

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