My research focuses on applied and computational mathematics, particularly on uncertainty quantification (UQ), modeling and system identification, inverse problems, and data-driven model learning. The main applications driving my research are from hydrology. More precisely, in my work, I am bridging the gap between theoretical work on High-dimensional Uncertainty Quantification and Bayesian Inversion, applied to relatively simple simulation models, and more complex real-world problems.
High-dimensional Forward Uncertainty Quantification and Sensitivity Analysis (mainly, analysis of conceptual distributed hydrologic models)
Sparse Grids Methods
Inverse problems - Bayesian Inference
Machine Learning
Open and running student projects
Runnin student projects
Erik A. Maurer: "Applying recurrent neural networks (RNNs) in the field of hydrology to explore uncertainty in time series forecasts and enhance theory-based models". School of Engineering and Design of the Technical University of Munich. Since June 2024
Danylo Movchan: "Large Scale Outdoor Scene Reconstruction with 3D Gaussian Splatting". Master's Thesis, CIT School - Computer Science Department; in collaboration with Stanford University. Since October 2024
Open student projects
If you are interested in a student project (Bachelor's or Master's Thesis or anything else), it is the best to contact me directly. Here is the list of some projects that I would offer at the moment (the list is not exhaustive):
"UQ and SA of Hydrologic model HBV using pyApprox software tool"
Do you want to know what other students are working on in our chair? You are warmly encouraged to attend their presentations at the SCCS Colloquium! Come to get ideas, meet your potential supervisor, or to learn from the style of others for your own presentation.
Boris Liu: Accounting for states and parameter uncertainty of the HBV-SASK hydrologic model using particle filtering as a sequential data assimilation technique. Bachelorarbeit, 2024 mehr…BibTeX
Chengjie Zhou: Efficient Bayesian Inference of Hydrological Model Parameters: Implementation of a Parallel Markov Chain Monte Carlo Approach. Bachelorarbeit, 2024 mehr…BibTeX
Volltext (mediaTUM)
Erik A. Maurer: Applying recurrent neural networks (RNNs) in the field of hydrology to explore uncertainty in time series forecasts and enhance theory-based models. Bachelorarbeit, 2024 mehr…BibTeX
Volltext (mediaTUM)
Markus Englberger: Using the Spatially Adaptive Combination Technique for Efficient Quantification of Uncertainty in Hydrological Models. Bachelorarbeit, 2022 mehr…BibTeX
Volltext (mediaTUM)
2021
Hanna Weigold: Second-Order Optimization Methods for Bayesian Neural Networks. Masterarbeit, 2021 mehr…BibTeX
Jonas Fill: Development of the Bayesian Recurrent Neural Network Architectures for Hydrological Time Series Forecasting. Bachelorarbeit, 2021 mehr…BibTeX
Volltext (mediaTUM)
Simon Zocholl: Development of Recurrent Neural Network Architectures for Hydrological Time Series Forecasting. Bachelorarbeit, 2021 mehr…BibTeX
Volltext (mediaTUM)
2020
Jonas Treplin: Parallel Evaluation of Adaptive Sparse Grids with Application to Uncertainty Quantification of Hydrology Simulations. Projektarbeit, 2020 mehr…BibTeX
Volltext (mediaTUM)
Mathieu Putz: Developing a prototype of Bayesian Inference framework to recalibrate the complex hydrological model LARSIM. Studienarbeit, 2020 mehr…BibTeX
Volltext (mediaTUM)
2019
Frank Schraufstetter: Development of a Prototype to Quantify the Uncertainty of the Water Balance Model LARSIM. Bachelorarbeit, 2019 mehr…BibTeX
Volltext (mediaTUM)
Vyshakh Unnikrishnan: Implementation of a deep learning based model for rainfall-runoff modelling. Masterarbeit, 2019 mehr…BibTeX
Other Supervised Student Projects
Leon Fiedler: "Sensitivity Analysis of a Deep Learning Model for Discharge Prediction in the Regen Catchment". Masterarbeit, Ingenieurfakultät Bau Geo Umwelt; 2020 [BibTeX] [Volltext (mediaTUM)],
Ivana Jovanovic and Severin Reiz scored the 1st and 2nd place in the Best Poster Jury Award at CoSaS meeting in Erlangen, Germany, among 70 posters.…
[weiterlesen]
Ivana Jovanovic Buha: Efficient Uncertainty Quantification and Global Sensitivity Analysis of Time-dependent outputs in Hydrology Modeling. (Vortrag / Sparse Grids and Applications Seminar 2024) 2024 mehr…
2023
Ivana Jovanovic Buha: SCCS Lehrstuhl Treffen. (Vortrag) 2023 mehr…
Ivana Jovanovic Buha; Michael Obersteiner; Tobias Neckel; Hans-Joachim Bungartz: Efficient Uncertainty Quantification and Global Time-Varying Sensitivity Analysis Using the Spatially Adaptive Combination Technique. SIAM Conference on Uncertainty Quantification (UQ22), SIAM, 2022Atlanta, Georgiamehr…
2021
Ivana Jovanovic Buha; Florian Künzner; Tobias Neckel; Hans-Joachim Bungartz: Efficient Uncertainty Quantification and Global Time-Varying Sensitivity Analysis of Conceptual Hydrological Model. SIAM Conference on Computational Science and Engineering (CSE21), SIAM, 2021Fort Worth, Texas, U.S.A.mehr…
Other activities
Organizational support for "BGCE Student Paper Prize" for the best paper at the SIAM CSE (2019, 2021)