SKM 2023 – wissenschaftliches Programm
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DY: Fachverband Dynamik und Statistische Physik
DY 17: Machine Learning in Dynamics and Statistical Physics I
DY 17.4: Vortrag
Dienstag, 28. März 2023, 10:45–11:00, ZEU 160
Bayesian deep learning for error estimation in the analysis of anomalous diffusion — •Henrik Seckler1 and Ralf Metzler1,2 — 1Institute for Physics & Astronomy, University of Potsdam, 14476 Potsdam-Golm, Germany. — 2Asia Pacific Centre for Theoretical Physics, Pohang 37673, Republic of Korea
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusion model and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a well-calibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.