SFB 1294
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    • Research Area A Theory and Algorithms
      • A01 Statistics for stochastic partial differential equations
      • A02 Long-time stability and accuracy of ensemble transform filter algorithms
      • A03 Sequential and adaptive learning under dependence and non-standard objective functions
      • A04 Nonlinear statistical inverse problems with random observations
      • A05 Combining non parametric statistical and probabilistic approaches for inference on cloud-of-points data
      • A06 Approximative Bayesian inference and model selection for stochastic differential equations (SDEs)
      • A07 Model order reduction for Bayesian inference
    • Research Area B Algorithms and Applications
      • B02 Inferring the dynamics underlying protrusion-driven cell motility
      • B03 Parameter inference and model comparison in dynamical cognitive models
      • B04 Parametric and nonparametric modeling of spatiotemporal change patterns in seismicity using Hawkes processes
      • B05 Attention selection and recognition in scene-viewing
      • B06 Novel methods for the 3D reconstruction of the dynamic evolution of the Van Allen belts using multiple satellite measurements
      • B07 Inferring active particle dynamics by data assimilation
      • B08 Continuous learning by integrating reinforcement learning and data assimilation to individualise drug treatments
      • B09 Neural network modelling of brain responses during language comprehension
    • Research Area Z Common Activities
      • Z03 Information Infrastructure for data assimilation
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Seminars in 2025

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16Jan2025

From Model to Market: Enhancing Phase 3 Design with MIDD

Jane Knöchel, InSilicoTrials, Sweden 2.9.2.2210:15 - 11:45

Model Informed Drug Development (MIDD) has revolutionized the way pharmaceutical companies approach Phase 3 clinical trials. By integrating predictive…

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26Feb2025

Reducing the complexity of phase-field models for cell motility

Hidemasa Ishii, University of Tokyo 2.28.1.00109:15 - 10:45

Amoeboid cell motility involves the dynamics of cell shape, position, and intracellular patterns. In the phase-field approach, an artificial field…

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10Mar2025

Bayesian model selection for partially observed second order Langevin dynamics

Yusuke Kato, University of Tokyo 2.28.1.00113:00 - 14:00

To describe and elucidate the mechanisms of cell motility, numerous SDE-based Langevin models have been proposed. However, the data-driven comparison…

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16May2025

RIKEN’s activities to integrate data assimilation and AI/ML

Takemasa Miyoshi, RIKEN, Japan 2.28.0.10810:15 - 11:45

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Current Colloquia
  • Overview Colloquia Summer Term 2025

SFB 1294

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Coordinated by the University of Potsdam

Institute of Mathematics

SFB 1294 – Data Assimilation
Institute of Mathematics
Karl-Liebknecht-Str. 24-25
14476 Potsdam - OT Golm

SFB1294[at]uni-potsdam.de
+49 331 977 203137

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