EnKF Workshop 2023: Adaptive localization in nonlinear ensemble transport filtering
This poster complements my talk at the EnKF Workshop 2023 of the same name, and focusses on topics that did not fit into the talk itself. In brief, we have developed an efficient map adaptation strategy which automatically identifies parsimonious triangular maps for ensemble transport filtering. This adaptation strategy can also detect conditional independence properties, which permits an efficient form of localization. However, the degree to which we can exploit these properties depends on variable ordering. We propose a modified version of the reverse Cuthill-McKee algorithm, which detects efficient orderings while respecting variable block structure.
AGU 2022: Nonlinear Ensemble Transport Smoothing
This is online interactive poster was presented at the AGU session "Advances in Data Assimilation, Predictability, and Uncertainty Quantification". It summarizes our research on ensemble transport smoothers (Part 1 and Part 2). In case the interactive poster is removed from the AGU website, I have also provided a recording of the poster's contents here.
AGU 2021: Transport Methods for Nonlinear Smoothing
This hybrid poster presents intermediate progress of my work on nonlinear smoothing with transport methods. We discuss two different smoothing strategies (joint-analysis and backward smoothers), often applied as Kalman-type algorithms. We then introduce transport methods as a pathway for nonlinear generalization of these smoothers. We demonstrate the efficacy of the resulting algorithm with the Lorenz-63 test case. The voiceover can be found here.
GQ 2019: Pseudo-Online Optimization of High-Conductivity Structures From Multiple-Point Statistics
This presentation showed a later stage of my work on the second chapter of my dissertation, in collaboration with Prof. Matteo Camporese from the University of Padova and Prof. Philippe Renard from the University of Neuchâtel. As part of the EU Horizon2020 INSPIRATION ITN, I also presented at this conference. At this stage, our work on the second chapter of my dissertation has progressed sufficient that we could already show prediction results for the optimized parameter fields.
AGU 2018: Nested Particle Filters for Data Assimilation and Sequential Parameter Optimization
This presentation showed an early stage of my work on the second chapter of my dissertation, in collaboration with Prof. Matteo Camporese from the University of Padova and Prof. Philippe Renard from the University of Neuchâtel. The goal of this research was to come up with a sequential optimization scheme for groundwater models with uncertain subsurface structures, as - for example - generated by multiple-point statistics.
EGU 2018: Joint data assimilation and parameter calibration in real-time groundwater modelling using nested particle filters
This poster explored early experiments to use particle filters for optimization of groundwater model parameters. However, instead of optimizing the parameters directly, we optimize a set of hyper-parameters used to generate the parameter fields used by the model. We explored what happens when geological features are accidentally misspecified. This was also the first poster for which I experimented with augmented reality elements. This poster won one of the 2018 EGU OSPP awards.
AGU 2017: Joint Data Assimilation and Parameter Calibration in online groundwater modelling using Sequential Monte Carlo techniques
My first presentation at a major scientific conference, the 2017 AGU Fall Meeting in New Orleans, early into my PhD. This poster shows early experiments with particle filters for parameter inference in hydrogeological models.