Prof. Phaedon-Stelios Koutsourelakis, Ph.D.
Academic Career and Research Areas
The aim of the research work of Professor Koutsourelakis (b. 1975) is to develop computational strategies to model and analyze physical and engineered systems with particular emphasis on continuum mechanics. Dr. Koutsourelakis adopts a cyber-enabled statistical discovery mindset in his research activities. These center around two main themes: a) uncertainty quantiﬁcation: identiﬁcation and modeling of stochastic aspects in the mechanics' systems by means of data assimilation and efficient procedures for their analysis and design in the presence of uncertainties, and b) multi-scale phenomena: the discovery of accurate macro-scale descriptions for processes evolving on vastly different spatio-temporal scales.
Professor Koutsourelakis completed his undergraduate studies at the National Technical University of Athens, Greece and obtained a PhD from Princeton University, NJ, USA. He has previously held academic positions at the University of Innsbruck (Leopold-Franzens) (Austria), Cornell University (USA) and Heriot-Watt University (UK). He was also a research scientist at the Lawrence Livermore National Laboratory, (USA).
- Dean’s First Year Merit Prize in Recognition of Outstanding Record, Princeton University (1998)
- Prize for Excellence in Academic Performance, National Technical University of Athens (1994,1997,1998)
Koutsourelakis PS, Bilionis I: “Scalable Bayesian reduced-order models for simulating high-dimensional multiscale dynamical systems”. SIAM Multiscale Modeling & Simulation. 2011; 9(1): 449-485.Abstract
Sternfels R, Koutsourelakis PS: “Stochastic design and control in random heterogeneous materials”. Journal of Multiscale Computational Engineering. 2011; 9(4): 425-443.Abstract
Koutsourelakis PS: “A multi-resolution, non-parametric, Bayesian framework for identiﬁcation of spatially-varying model parameters”. Journal of Computational Physics. 2009; 228(17): 6184-6211.Abstract
Koutsourelakis PS: “Accurate uncertainty quantiﬁcation using inaccurate models”. SIAM Journal on Scientiﬁc Computing. 2009; 31(5): 3274-3300.Abstract
Koutsourelakis PS: “Stochastic Upscaling in Solid Mechanics: An exercise in machine learning”. Journal of Computational Physics. 2007; 226(1): 301-325.Abstract