Antonis Kamariotis, M.Sc. (Hons)

Room: N3632

Phone: +49 89 289 23053

E-Mail: antonis.kamariotis@tum.de

Office hours: by arrangement

Curriculum Vitae

  • Since 2019 | Ph.D. Student at the Engineering Risk Analysis Group, Technische Universität München. In collaboration with the Structural Mechanics and Monitoring Group, ETH Zürich.
  • 2017 - 2019 | Student of the Bavarian Graduate School of Computational Engineering (BGCE), Honors Program.
  • 2016 - 2019 | M.Sc. (Hons) in Computational Mechanics, Technische Universität München.
  • 2010 - 2016 | Diploma in Civil Engineering, Aristotle University of Thessaloniki.

Research

  • Decision support for Structural Health Monitoring
  • Value of Information
  • Bayesian model updating

Teaching

  • WS20/21: Teaching assistant for the lecture "Structural Reliability", TUM.
  • WS19/20: Teaching assistant for the lecture "Structural Reliability", TUM.
  • SS19: Teaching assistant for the lecture “Risk Assessment”, TUM.

Supervised theses/projects

  • Master thesis: Luca Sardi (2020-2021) – Particle filtering for joint deterioration state-parameter estimation with indirect measurements
  • Master thesis: Wanchang Zhang (2020) – A cross entropy based importance sampling scheme for value of information analysis
  • Honors Project: Wanchang Zhang (2020) – Modal-based damage localization on a bridge-type monitored structure.

Publications

  • Kamariotis, A., Straub, D., Chatzi, E. (2020). Optimal maintenance decisions supported by SHM: A benchmark study. In: Seventh International Symposium on Life-Cycle Civil Engineering 2020, Shanghai, China.
  • Antonis Kamariotis, Giulia Antinori, Iason Papaioannou, Fabian Duddeck (2019). Mixed aleatory-epistemic uncertainty quantification and sensitivity analysis. 17th International Probabilistic Workshop (IPW2019), Edinburgh, Scotland.

Talks

  • Mixed aleatory-epistemic uncertainty quantification and sensitivity analysis. 17th International Probabilistic Workshop (IPW2019), Edinburgh, Scotland.
  • Optimal maintenance decisions supported by SHM: A benchmark study. In: Seventh International Symposium on Life-Cycle Civil Engineering 2020, Online conference.
  • Calibration of fatigue life models based on Bayesian methods. 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP), Crete, Greece, June 24-26.