Decoupling Models and UQ Workflows with UM-Bridge
Modern uncertainty quantification (UQ) workflows often depend on complex computational models that are tightly coupled to specific inference algorithms, making experimentation, reuse, and long-term maintenance difficult. UM-Bridge (Uncertainty Quantification and Modeling Bridge) addresses this challenge by providing a lightweight, language-agnostic interface between models and UQ methods. Using a simple HTTP-based protocol, UM-Bridge allows forward models written in any language to be exposed as services and accessed by a wide range of UQ tools without modification. This decoupling simplifies development, improves reproducibility, and enables collaboration across domains and software ecosystems. In this talk, I will give an overview of the UM-Bridge interface and highlight recent developments in the software.
The capabilities of UM-Bridge are demonstrated through a case study on Bayesian inversion for tsunami source detection using buoy or hydrophone data. Our initial approach relied on a monolithic software stack that tightly integrated the forward model and UQ components. The forward problem was solved using an ADER-DG method, while inference was performed with a multilevel Markov Chain Monte Carlo (MLMCMC) algorithm. This approach proved difficult to scale due to complex dependencies in the parallelisation approach and HPC infrastructure.
To overcome these limitations, we transitioned to a modular architecture enabled by UM-Bridge. This approach allows the use of Gaussian Process surrogate models as efficient coarse-level approximations and supports more advanced MCMC strategies within a multilevel framework. It enables the incorporation of richer physical descriptions such as acoustic wave propagation. I will conclude the talk by discussing recent developments in this application and their implications for tsunami source detection and uncertainty-aware early warning systems.
