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Cardiovascular Digital Twins: From Physics to Data
US & West ·
This review examines approaches to creating digital twins of the cardiovascular system: mechanistic models with physiological interpretability but high computational costs, and data-driven methods with better scalability but limited robustness. Hybrid approaches, including physics-informed and graph-based methods, are described, along with validation challenges and pathways to clinical deployment.
Why it matters
Important for understanding the applicability limits and prospects of hybrid modeling methods in medicine, relevant to the development of digital twins of complex biological systems.
arXiv:2609.09380v1 Announce Type: cross Abstract: Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model predictive control (MPC) solvers can handle the fast system dynamics and…
Digital twin simulation models are evolved and redeployed like software, yet DEVS-based engines offer a sound formal basis with little support for versioning, automated validation, or continuous delivery in cloud-native environments, leaving model lifecycle management ad hoc in most deployments.
arXiv:2512.13229v2 Announce Type: replace Abstract: As cyber-physical systems (CPSs) become more dependent on data and communication networks, their vulnerability to false data injection (FDI) attacks has raised significant concerns.
