Bayesian Emulation for Computer Models with Multiple Partial Discontinuities

TL;DR

This paper introduces the TENSE framework, a novel emulation method for complex computer models with multiple known discontinuities, enabling efficient uncertainty quantification without splitting the input space.

Contribution

The paper presents the TENSE framework that models multiple partial discontinuities in computer models using specialized correlation structures, improving emulation efficiency.

Findings

Successfully emulated the OLYMPUS reservoir model with multiple discontinuities.

The TENSE framework allows simultaneous updating and efficient design of emulators.

Avoids the need to partition input space into subregions.

Abstract

Computer models are widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical construct that mimics the slow to evaluate computer model, and greatly aids the vastly more computationally intensive uncertainty quantification calculations that an important scientific analysis often requires. We examine the problem of emulating computer models that possess multiple, partial discontinuities occurring at known non-linear location. We introduce the TENSE framework, based on carefully designed correlation structures that respect the discontinuities while enabling full exploitation of any smoothness/continuity elsewhere. This leads to a single emulator object that can be updated by all runs simultaneously, and also used for efficient design. This approach avoids having to split the input space into multiple subregions. We apply the TENSE framework to the TNO Challenge II, emulating the OLYMPUS reservoir model, which possess multiple such discontinuities.