TY  - CONF
A1  - Zhang, Yi
A1  - Mikelsons, Lars
T1  - Solving stochastic inverse problems with stochastic BayesFlow
T2  - 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 28-30 June 2023, Seattle, WA, USA
N2  - Normalizing flows have gained increasing attention in the area of probabilistic modeling. For solving inverse problems, BayesFlow is a state-of-the-art Bayesian inference method based on normalizing flows. However, BayesFlow suffers from overfitting in many real-world scenarios. Therefore, we put forward stochastic BayesFlow, enhancing BayesFlow through stochastic normalizing flows. Apart from being less prone to overfitting, stochastic BayesFlow performs more robustly in parameter identification from noisy observations. Moreover, we develop a stochastic BayesFlow algorithm to solve stochastic inverse problems and validate it using the inverse uncertainty quantification of a simulated vehicle dynamics model.
Y1  - 2023
UR  - https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/119929
DO  - 10.1109/AIM46323.2023.10196190
SN  - 978-1-6654-7633-1
SP  - 966
EP  - 972
PB  - IEEE
CY  - Piscataway, NJ
ER  -