Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling

Guzzetti, Sofia; Alvarez, L.A. Mansilla; Blanco, Pablo; Carlberg, Kevin; Veneziani, Alessandro

doi:10.1016/j.cma.2019.112626

Cited by 24 publications

(9 citation statements)

References 43 publications

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“…The most common example, in this special issue alone, is the theme of learning model parameters, ranging from characterizing brain stiffness [124] to cardiac conductivity [9]. Another immediate application in biomedicine is using machine learning to create fast and robust surrogate models, as discussed in this special issue for the examples of cardiac electrophysiology [107], vascular hemodynamics [39], and skin growth [61]. As we will see, these surrogate models allow us to seamlessly integrate multimodality or multi-fidelity data.…”

Section: Motivationmentioning

confidence: 99%

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng

Alber

Tepole³

et al. 2020

Arch Computat Methods Eng

245

103

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Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. This review serves as introduction to a special issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems that will help identify current roadblocks and areas where computational mechanics, as a discipline, can play a significant role. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

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Section: Motivationmentioning

confidence: 99%

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Peng

Alber

Tepole³

et al. 2020

Arch Computat Methods Eng

245

103

View full text Add to dashboard Cite

show abstract

“…In addition to forbidding runtimes of any one or more components that constrain model uncertainty assessments, individual model components may also be managed by different groups, with varying computational software and hardware, which can hinder cohesive and automated modeling ( Buahin et al, 2019 ). Water quality modeling efforts can benefit from leveraging recently developed methods in computational mathematics and engineering that decompose system uncertainty analysis into uncertainty analysis of individual model components that can be performed in parallel, thereby allowing those analyses to be combined resourcefully to assess system level uncertainties ( Amaral et al, 2014 ; Guzzetti et al, 2020 ; Sankararaman & Mahadevan, 2012 ).…”

Section: Improvements In the Modeling Processmentioning

confidence: 99%

Modeling Water Quality in Watersheds: From Here to the Next Generation

Horsburgh

Jakeman

et al. 2020

Water Resources Research

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In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

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“…Table 2 presents the experimental data. Applying the uncertainty expansion theory [1] , [2] , [3] , [4] and using the certificate of calibration [5] was possible to calculate the uncertainty associated to the standard mass. Moreover, Table 2 shows, in highlight, the indicated mass by the analytical scale, the apparent mass and its uncertainty.…”

Section: Data Descriptionmentioning

confidence: 99%

Evaluation of the metrological reliability of a graduated cylinder from experimental data from an in-situ calibration

Vásquez¹,

Yepes²,

Rodriguez-Salas³

et al. 2020

Data in Brief

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This work presents the data experimentally collected in a chemical laboratory for the calibration of a graduated cylinder. There are several factors that can influence the volume measurement using this type of instrument and, consequently, its metrological reliability, for example: the internal geometry, the environmental conditions (ambient temperature, atmospheric pressure and relative humidity), the acceleration of gravity, the density of the air, among others. For the data collection it was necessary to use a glass liquid thermometer (Range: 0–10 °C), a digital thermohygrometer (Range: 0–100 °C and 0–99%RH) and a digital barometer (Range: 0–9999 mbar). Additionally, an analytical scale (Range: 0–220 g) was used for mass measurement. From the measurements obtained, it was possible to determine the in-situ air density and the buoyancy factor that influences the mass measurement. The data, rigorously obtained, present a potential use to determine the metrological reliability of a graduated cylinder for laboratory use and, additionally, contribute to perform a metrological validation of alternative methods for the calibration of graduated cylinder.

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Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling

Cited by 24 publications

References 43 publications

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Modeling Water Quality in Watersheds: From Here to the Next Generation

Evaluation of the metrological reliability of a graduated cylinder from experimental data from an in-situ calibration

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