Computational Inverse Problems

The goal of this MATRIX program on “computational inverse problems” is to address open challenges and recent advancements in computational methods for solving large-scale inverse problems, which is considered as one of the driving forces for integrating large and complex data sets into large-scale computational models. This program will cover a wide range of relevant topics including deterministic and statistical inverse problems, Bayesian analysis, computational statistics (e.g., Markov chain Monte Carlo, Sequential Monte Carlo), high-dimensional approximation techniques (e.g., quasi Monte Carlo and sparse grids), algorithms for big data analytics, stochastic and distributed optimization methods, model reduction, high performance computing, and scientific and industrial applications such as computational finance, mathematical biology, geophysics and flows in porous media, etc. This workshop aims to provide a forum for Australian and international researchers to share the latest developments in these fields and to promote innovative collaborations across multiple research interfaces and geographical borders. The combined perspectives of tentative participants will allow us to identify promising future research directions in the field of computational inverse problems and its applications.