Research focus
- Physics-aware machine learning
- Uncertainty Quantification
- Surrogate modeling
- Stochastic partial differential equations
- Reduced-order modeling
- Random materials
Teaching
- Continuum Mechanics (MSE) WS 15/16 - WS 18/19: Tutorial and Kleingruppenübung
- Uncertainty Quantification (Journal Club): SS 16 - SS 19
- Modellierung von Unsicherheiten und Daten im Maschinenwesen SS 18 - SS 19: Kleingruppenübung
Published papers
- Constantin Grigo, Phaedon-Stelios Koutsourelakis: A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
- Constantin Grigo, Phaedon-Stelios Koutsourelakis: Bayesian model and dimension reduction for uncertainty propagation: applications in random media
- Constantin Grigo, Phaedon-Stelios Koutsourelakis: A data-driven model order reduction approach for Stokes flow throughrandom porous media
- Constantin Grigo, Phaedon-Stelios Koutsourelakis: Probabilistic Reduced-Order Modeling for Stochastic Partial Differential Equations
Conference contributions
| ECCOMAS | 2016: | Multi-fidelity, model-based stochastic optimization: applications in random media |
| Big Data | 2017: | Probabilistic reduced-order modeling of stochastic partial differential equations |
| SIAM CSE | 2017: | Probabilistic, Coarse-Grained Models for PDEs with Random Coefficients |
| UNCECOMP | 2017: | Probabilistic reduced-order modeling for stochastic partial differential equations |
| FrontUQ | 2017: | Probabilistic reduced-order modeling for stochastic partial differential equations |
| GAMM | 2018: | A data-driven model order reduction approach for Stokes flow through random porous media |
| SIAM UQ | 2018: | A Bayesian Coarse-Graining Approach to the Solution of Stochastic Partial Differential Equations |
| WCCM | 2018: | A Bayesian Encoder-Decoder Model Order Reduction Approach for Problems in Random Heterogeneous Media |
| SIAM CSE | 2019: | Physics-constrained Surrogates for Reduced-order Modeling and Uncertainty Quantification |