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3D multi-physics uncertainty quantification using physics-based machine learning.

ABSTRACT: Quantitative predictions of the physical state of the Earth's subsurface are routinely based on numerical solutions of complex coupled partial differential equations together with estimates of the uncertainties in the material parameters. The resulting high-dimensional problems are computationally prohibitive even for state-of-the-art solver solutions. In this study, we introduce a hybrid physics-based machine learning technique, the non-intrusive reduced basis method, to construct reliable, scalable, and interpretable surrogate models. Our approach, to combine physical process models with data-driven machine learning techniques, allows us to overcome limitations specific to each individual component, and it enables us to carry out probabilistic analyses, such as global sensitivity studies and uncertainty quantification for real-case non-linearly coupled physical problems. It additionally provides orders of magnitude computational gain, while maintaining an accuracy higher than measurement errors. Although in this study we use a thermo-hydro-mechanical reservoir application to illustrate these features, all the theory described is equally valid and applicable to a wider range of geoscientific applications.

SUBMITTER: Degen D

PROVIDER: S-EPMC9582207 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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3D multi-physics uncertainty quantification using physics-based machine learning.

Degen Denise D Cacace Mauro M Wellmann Florian F

Scientific reports 20221019 1

Quantitative predictions of the physical state of the Earth's subsurface are routinely based on numerical solutions of complex coupled partial differential equations together with estimates of the uncertainties in the material parameters. The resulting high-dimensional problems are computationally prohibitive even for state-of-the-art solver solutions. In this study, we introduce a hybrid physics-based machine learning technique, the non-intrusive reduced basis method, to construct reliable, sca ...[more]

PMID: 36261601

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Project description:Digital phantoms are important tools for optimization and evaluation of x-ray imaging systems, and should ideally reflect the 3D structure of human anatomy and its potential variability. In addition, they need to include a good level of detail at a high enough spatial resolution to accurately model the continuous nature of the human anatomy. A pipeline to increase the spatial resolution of patient-based digital breast phantoms that can be used for computer simulations of breast imaging is proposed. Given a tomographic breast image of finite resolution, the proposed methods can generate a phantom and increase its resolution at will, not only simply through super-sampling, but also by generating additional random glandular details to account for glandular edges and strands to compensate for those that may have not been detected in the original image due to the limited spatial resolution of the imaging system used. The proposed algorithms use supervised learning to predict the loss in glandularity due to limited resolution, and then to realistically recover this loss by learning the mapping between low and high resolution images. They were trained on high-resolution synchrotron images (detector pixel size 60 ?m) reconstructed at seven voxel dimensions (60 ?m-480 ?m), and applied to patient images acquired with a clinical breast CT system (detector pixel size 194 ?m) to generate super-resolution phantoms (voxel sizes 68 ?m). Several evaluations were made to assess the appropriateness of the developed methods, both with the synchrotron (relative prediction error 0.010??±??0.004, recovering accuracy 0.95??±??0.04), and with the clinical images (average glandularity error at 194 ?m: 0.15%??±??0.12%). Finally, a breast radiologist assessed the realism of the developed phantoms by blindly comparing original and phantom images, resulting in not being able to distinguish the real from the phantom images. In conclusion, the proposed method can generate super-resolution phantoms from tomographic breast patient images that can be used for future computer simulations for optimization of new breast imaging technologies.

Dataset Information

3D multi-physics uncertainty quantification using physics-based machine learning.

Publications

3D multi-physics uncertainty quantification using physics-based machine learning.

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