Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis

Nikishova, Anna; Veen, Lourens; Zun, Pavel; Hoekstra, Alfons G.

doi:10.1098/rsta.2018.0154

Cited by 16 publications

(18 citation statements)

References 25 publications

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“…Similarly, naive non-intrusive sampling methods may not be an efficient route to quantifying uncertainties in multiscale applications, particularly with respect to HPC codes with wildly varying resource requirements, and may additionally miss pathological behaviour introduced by complex (sometimes conditional) coupling of submodels. Semi-intrusive Monte Carlo methods have been explored as a more efficient method for applications characterised by a large asymmetry in the computational expense of the submodels[113].…”

mentioning

confidence: 99%

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

Vassaux

Sinclair

Richardson

et al. 2019

Advcd Theory and Sims

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The current approach to materials discovery and design remains dominated by experimental testing, frequently based on little more than trial and error. With the advent of ever more powerful computers, rapid, reliable and reproducible computer simulations are beginning to represent a feasible alternative. As high performance computing reaches the exascale, exploiting the resources efficiently presents interesting challenges and opportunities. Multiscale modelling and simulation of materials is an extremely promising candidate for exploiting these resources based on the assumption of a separation of scales in the architectures of nanomaterials. We present examples of hierarchical and concurrent multiscale approaches which benefit from the weak scaling of monolithic applications, thereby efficiently exploiting large scale computational resources. We discuss several multiscale techniques, incorporating the electronic to the continuum scale, which can be applied to the efficient design of a range of nanocomposites. We then discuss our work on the development of a software toolkit designed to provide verification, validation and uncertainty quantification to support actionable prediction from such calculations.

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mentioning

confidence: 99%

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

Vassaux

Sinclair

Richardson

et al. 2019

Advcd Theory and Sims

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“…These guidelines only refer to medical devices and pharmacokinetics, thus future efforts are needed to define suitable protocols and methods for wider biomedical applications. The criticality of computational model verification, uncertainty quantification, calibration and validation in the biomedical field is also demonstrated by recent publications (Marino et al, 2008;Luraghi et al, 2018;Nikishova et al, 2018Nikishova et al, , 2019Fleeter et al, 2020;Ye et al, 2021a;Curreli et al, 2021;Groen et al, 2021;Luraghi et al, 2021;Rapadamnaba et al, 2021).…”

Section: Challenges and Future Directions Verification Uncertainty Quantification Calibration And Validationmentioning

confidence: 96%

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

Corti

Colombo

Migliavacca

et al. 2021

Front. Bioeng. Biotechnol.

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The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

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“…The VECMA toolkit is already being applied in several circumstances: climate modelling, where multiscale simulations of the atmosphere and oceans are required; forecasting refugee movements away from conflicts, or as a result of climate change, to help prioritize resources and investigate the effects of border closures and other policy decisions [49]; for exploring the mechanical properties of a simulated material at several length and time scales with verified multiscale simulations; and multiscale simulations to understand the mechanisms of heat and particle transport in fusion devices, which is important because the transport plays a key role in determining the size, shape and more detailed design and operating conditions of a future fusion power reactor, and hence the possibility of extracting almost limitless energy; and verified simulations to aid in the decision-making of drug prescriptions, simulating how drugs interact with a virtual version of a patient's proteins, [50] or how stents will behave when placed in virtual versions of arteries [51]. The toolkit has also been used to demonstrate the very considerable uncertainty in the predictions arising from the CovidSim code used to make predictions of death rates caused by the COVID-19 pandemic [52,53].…”

Section: Modelling and Simulationmentioning

confidence: 99%

When we can trust computers (and when we can't)

Coveney

Highfield

2021

Phil. Trans. R. Soc. A.

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With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico ’.

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Semi-intrusive multiscale metamodelling uncertainty quantification with application to a model of in-stent restenosis

Cited by 16 publications

References 25 publications

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

When we can trust computers (and when we can't)

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