- Tan Bui-thanh, The University Of Texas At Austin
- Andreas Mang, University of Houston
Inverse, machine-learning, and big-data problems are widespread in computational sciences and engineering. Despite formidable advances in recent years on all frontiers, ranging from pure mathematics to computational sciences, significant challenges remain, especially when it comes to addressing data-driven problems. In inverse/learning problems, parameters are typically related to indirect measurements by a system of partial differential equations (PDEs) or a neural network, which could be highly nonlinear and non-convex. Available indirect data are often noisy and subject to natural variation, while the unknown parameters of interest are high dimensional or possibly infinite-dimensional in principle. Bayesian inference provides a systematic framework that allows us to rigorously quantify the uncertainty in the inverse/machine-learning problem, and to assess model validity and adequacy. Since the amount of data we wish to process is only going to increase for the foreseeable future, there is a critical need for effective algorithms that integrate data with simulations and learning approaches that are computation- and data-scalable. This minisymposium aims to attract researchers at the forefront of inverse and learning problems, data science, and data-intensive problems to present their latest work on computation- and data-scalable algorithms in inverse and machine learning problems..