Posters

AGU 2021: Transport Methods for Nonlinear Smoothing

AGU Fall Meeting 2021 https://agu.confex.com/agu/fm21/webprogrampreliminary/Paper941401.html DownloadRamgraber, M., Baptista, R., McLaughlin, D., and Marzouk, Y. (2021)

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

10th International Groundwater Quality Conference (GQ19) https://www.uee.uliege.be/cms/c_4476800/fr/presentations-et-posters-gq2019 DownloadRamgraber, M., Camporese, M., Renard, P., and Schirmer, M. (2019)

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

AGU Fall Meeting 2018 https://scholar.google.com/scholar?oi=bibs&cluster=16136839413738041387&btnI=1&hl=de DownloadRamgraber, M., Camporese, M., Renard, P., and Schirmer, M. (2018)

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

EGU General Assembly 2018 https://www.egu.eu/awards-medals/ospp-award/2018/maximilian-ramgraber/ DownloadRamgraber, M., and Schirmer, M. (2018)

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

AGU Fall Meeting 2017 https://ui.adsabs.harvard.edu/abs/2017AGUFM.H31D1535R/abstract DownloadRamgraber, M., and Schirmer, M. (2017)

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.