M7. Bayesian, Variational, and Optimization Techniques for Inverse Problems
in Stochastic Partial Differential Equations

In recent years, significant attention has been given to developing stochastic models and methods for inverse and control problems associated with stochastic and deterministic equations. Important applications motivate estimating stochastic parameters, source terms, or boundary conditions in stochastic partial differential equations. On the other hand, recent developments in machine learning and related subjects have advocated the utility of stochastic models not only for stochastic inverse problems but also for deterministic identification and control problems. This mini-symposium aims to bring together well-known experts and young researchers in the dynamic and expanding field of stochastic inverse problems.

The main topics include the Bayesian approach, variational and optimization techniques, stochastic approximation, stochastic gradient, neural networks, Kalman filter, and related methods aimed at studying inverse problems associated with stochastic partial differential equations.

Organizers:
Barbara Kaltenbacher, University of Klagenfurt, Austria, This email address is being protected from spambots. You need JavaScript enabled to view it.
Akhtar A. Khan, Rochester Institute of Technology, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Hans-Jörg Starkloff, Technische Universität Bergakademie Freiberg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Christiane Tammer, Martin-Luther-University of Halle-Wittenberg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.

Invited Speakers: 
Ghada Alobaidi, American University of Sharjah, UAE, This email address is being protected from spambots. You need JavaScript enabled to view it.
Stochastic dynamics of influenza infection: Qualitative analysis and numerical results

Olalekan Babaniyi, Rochester Institute of Technology, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Evaluating the accuracy of the posterior probability distribution in an elastic inverse problem

Annamaria Barbagallo, University of Naples Federico II (UNINA), Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.
A control economic equilibrium problem via inverse stochastic variational inequalities

Kevin Bitterlich, TU Bergakademie Freiberg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Towards Multilevel Slice Sampling for Bayesian inverse problems

Simon Cotter, The University of Manchester, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Bayesian data selection

Martin Eigel, Weierstrass Institute, Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Multilevel normalizing flows for inverse problems with parametric PDE

Daniel Gendin, University at Buffalo, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Uncertainty in inverse elasticity problems

Phuoc Truong Huynh, University of Klagenfurt, Austria, This email address is being protected from spambots. You need JavaScript enabled to view it.
Optimality of pulse energy for photoacoustic tomography

Marco Iglesias Hernandez, University of Nottingham, Nottingham, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Ensemble Kalman inversion for Bayesian shape identification

Baasansuren Jadamba, Rochester Institute of Technology, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
On a parameter identification problem in elliptic PDEs

Akhtar A. Khan, Rochester Institute of Technology, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Identification of random parameters in stochastic variational inequalities

Taufiquar Khan, University of North Carolina at Charlotte, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Optimal Bayesian image reconstruction in Diffuse Optical Tomography

Qi Lu, Sichuan University, P.R. China, This email address is being protected from spambots. You need JavaScript enabled to view it.
Recent progress on optimal control of Stochastic Partial Differential Equations

Stanisław Migórski, Jagiellonian University in Krakow, Poland, This email address is being protected from spambots. You need JavaScript enabled to view it.
Inverse problems for parabolic variational-hemivariational inequalities with unilateral constraints

Miguel Sama, Universidad Nacional de Educación a Distancia, Spain, This email address is being protected from spambots. You need JavaScript enabled to view it.
Uncertainty quantification in residential thermal models

Hans-Joerg Starkloff, TU Bergakademie Freiberg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Stochastic elliptic inverse problems as abstract elliptic inverse problems

Christiane Tammer, Martin Luther University of Halle-Wittenberg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Existence results and optimality conditions in regularization techniques for inverse problems

Hans-Werner van Wyk, Auburn University, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Adaptivity in parameter resolution for Bayesian inverse problems by stochastic proximal iterations

Tim Wildey, Sandia National Laboratories, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
A scalable variational approach for solving data-consistent stochastic inverse problems