论文标题
具有多个部分不连续性的计算机模型的贝叶斯仿真
Bayesian Emulation for Computer Models with Multiple Partial Discontinuities
论文作者
论文摘要
计算机模型在一系列科学学科中广泛使用，以描述各种复杂的物理系统，但是要执行通常需要采用模拟器的完全不确定性量化。模拟器是一种快速的统计结构，模仿了评估计算机模型的缓慢，并且极大地帮助了重要的计算密集型不确定性量化计算，而这些计算通常需要进行重要的科学分析。我们研究了模拟在已知的非线性位置出现多个部分不连续性的计算机模型的问题。我们基于精心设计的相关结构，介绍了时态框架，这些结构尊重不连续性，同时可以充分利用其他地方的任何平滑度/连续性。这导致一个单个模拟器对象，可以同时通过所有运行来更新，还用于有效设计。这种方法避免了必须将输入空间拆分为多个子区域。我们将时态框架应用于TNO挑战II，并模仿具有多种此类不连续性的奥林巴斯储层模型。
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.
