FWF Elise Richter Grant V1000

Computational Uncertainty Quantification in Nanotechnology          PI: Leila Taghizadeh

 

The goals of this project include

1) to investigate how the uncertainty propagates through the mathematical models in nanoelectronical devices and effects the output (stochastic modeling),

2) to estimate the unknown parameters in the model using statistical Bayesian inversion and probabilistic methods (model calibration), and

3) optimal experimental design in nanotechnology.

Topics for Bachelor's and Master's Theses

  • MCMC methods for high-dimensional Bayesian inverse problems
  • Sequential Bayesian optimal experimental design
  • Neural network surrogate methods for Bayesian inverse problems
  • Multilevel methods for Bayesian optimal experimental design

       Interested? Simply get in touch with me!

         leila.taghizadeh@tuwien.ac.at

Supervised Students:

  • Manuel Gehmeyr. Bayesian Reconstruction and Optimal Experimental Design for Multispectral Optoacoustic Tomography (Co-supervised at TU Munich, 2022)

  • Beatrix Rahnsch. Network-based Inference for the Prediction of the COVID-19 Spread  (TU Munich, 2023)

  • Francesca Barbaro. Sequential pCN-MCMC, an efficient MCMC method for Bayesian inversion of high-dimensional multi-Gaussian priors (TU Wien, WS 2023/24)
  • Georg Schwarz. Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (TU Munich, WS 2022/23)
  • Neel Misciasci.  Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design (TU Munich, WS 2022/23)
  • Thor Daniel Holm Fridbjörnsson. The old and the new: Can physics-informed deep learning replace traditional linear solvers? (TU Munich, WS 2021/22)