Session

DescriptionUncertainty quantification (UQ) has always been the pinnacle for the calibration and validation of scientific models. The steady increase in computational power is gradually enabling UQ even for very computationally expensive numerical simulations. However, the heterogeneity and the nonlinearity of the models, the need for hardware and programming language agnostic model interfaces, and the growing amount of available experimental data of diverse quality, pose a number of challenges to the development of UQ frameworks for HPC. In addition, plain embarrassingly parallel Bayesian inference algorithms (for instance, independent Markov chains) are being outcompeted by more efficient and adaptive, however, also more communication-intensive methods such as EMCEE, TMCMC, and SMC (optionally coupled with complex nonlinear particle filtering procedures) or Approximate Bayesian Computation. Driven by recent developments in these fields, we aim with this minisymposium to gather the developers of state-of-the-art, modular, scalable, and hardware-independent UQ frameworks together with application domain scientists. Our explicit goals are to expose researchers from different fields to the available parallel tools for UQ, and to discuss the challenges relevant to performing UQ on complex models on modern HPC hardware.