M22. Uncertainty Quantification for Inverse Problems: Methods, Software and Applications

Inverse problems arise in all areas of science, engineering, and industry, enabling the recovery of hidden parameters or structures from indirect and often noisy data. While in developing solution techniques significant advances have been achieved, assessing the uncertainty and reliability of these solutions remains a central theoretical and computational challenge.

This minisymposium brings together researchers working at the interface of inverse problems and uncertainty quantification (UQ). The talks will cover recent developments in Bayesian and deterministic approaches to UQ, advances in scalable and high-performance computational methods, and progress in open-source software frameworks that make these techniques more accessible. In addition, the symposium will highlight applications that demonstrate the impact of UQ in inverse problems in fields such as medical imaging, geophysics, materials science, and machine learning.

Organizers: 
Babak Maboudi Afkham, University of Oulu, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Christina Schenk, IMDEA Materials Institute, Spain, This email address is being protected from spambots. You need JavaScript enabled to view it.
Mirjeta Pasha, Virginia Tech, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.

Invited Speakers:
Babak Maboudi Afkham, University of Oulu, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Inhomogeneous priors for Bayesian inverse problems

Kenji Amaya, Tokyo Institute of Technology, Japan,   This email address is being protected from spambots. You need JavaScript enabled to view it.
Industrial inverse problems: from mathematical modeling to practical system implementation

John Darges, Emory University, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Neural optimal design of experiment for inverse problems

Ruben Harris, Freie Universität Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Analysis of ensemble Kalman inversion with time-dependent linear forward operators

Mirjeta Pasha, Virginia Tech, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Priorconditioned sparsity-promoting projection methods for deterministic and Bayesian linear inverse problems

Lassi Roininen, LUT University, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Uncertainty quantification for electrical impedance tomography using quasi-Monte Carlo methods

Christina Schenk, IMDEA Materials Institute, Spain, This email address is being protected from spambots. You need JavaScript enabled to view it.
Advanced surrogates for Bayesian calibration in complex inverse problems

Leila Taghizadeh, TU Wien, Austria, This email address is being protected from spambots. You need JavaScript enabled to view it.
A Markov chain Monte Carlo approach for Bayesian inverse problems with limited data

Andrea Tonini, Politecnico di Milano, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.
Uncertainty quantification variational autoencoders in Bayesian inverse problems for patient-specific cardiocirculatory models