Verification and validation of digital twins: a systematic literature review for manufacturing applications

Bitencourt, Julia; Wooley, Ana; Harris, Gregory

doi:10.1080/00207543.2024.2357741

International Journal of Production Research

2024

DOI: 10.1080/00207543.2024.2357741

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Verification and validation of digital twins: a systematic literature review for manufacturing applications

Julia Bitencourt,

Ana Wooley,

Gregory Harris

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2024

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Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Kemkar,

Tao,

Ghosh

et al. 2024

Front. Physiol.

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Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

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Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Kemkar,

Tao,

Ghosh

et al. 2024

Front. Physiol.

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Verification and validation of digital twins: a systematic literature review for manufacturing applications

Cited by 1 publication

References 181 publications

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

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