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An adaptive physics refinement framework to investigate red blood cells’ impact on adhesive tumor cell transport
Red blood cells play a dominant mechanical role in blood flow and strongly influence the transport and arrest of circulating tumor cells during metastasis. Circulating tumor cells (CTCs) are subject to physical interactions with blood components and adhesive interactions with endothelial cells, both of which critically influence their margination and likelihood of arrest. However, the…
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Instrumenting Lightweight, Modular Machine Learning Training and Inference in Parallel Solvers
Recent advances in exascale computing have increased the resolution and fidelity of large-scale simulations, while rapid progress in deep learning has accelerated efforts to couple machine learning with physics-based solvers. We present a lightweight, modular in situ coupling methodology that embeds machine learning training and inference directly into simulation workflows using the ParaView and Catalyst…
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Duke’s digital twin technology could take hemodynamic assessments to a whole other level
Amanda Randles, PhD, director of the Duke Center for Computational and Digital Health Innovation and professor of biomedical engineering, and her laboratory have developed HARVEY, a computational fluid dynamics platform that creates personalized models of blood flow from medical imaging.
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Digital Twins Modeling Blood Flow Could Predict Heart Problems
The HARVEY platform leverages supercomputing, physics-based simulations, and wearable device data to monitor real-time hemodynamics and spot heart failure early.
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How a Duke-led HPC method brought cell-scale cancer simulation to a single cloud node
Even on the largest supercomputers, simulations that explicitly resolve every blood cell can only cover a small volume. Amanda Randles and her team at Duke University set out to solve this problem by creating a physics-based technique called Adaptive Physics Refinement (APR) that can capture a much larger stretch of vessel by splitting the domain into a high-resolution ‘moving window’ and a low-resolution bulk fluid.
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The Changing Role Of Computational Mechanics In Drug Discovery
Center director Amanda Randles explains how the role of computational models in drug discovery is beginning to change.
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AI-powered methods shake up animal testing
Center director Amanda Randles is featured in this Nature Biology article about the FDA’s drive to reduce animal testing in drug development through AI-powered biosimulation techniques such as multi-agent virtual scientists and digital twins.
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Retrospective on the Lax Report: Then and Now
More than four decades after its release, the 1982 Lax Report remains a landmark blueprint for U.S. high-performance computing (HPC) policy. This retrospective revisits the report’s four principal recommendations, evaluating each on its current fulfillment. While the HPC ecosystem has matured through network advances, broader access, and the integration of artificial intelligence (AI) and distributed…
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Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling
Patient-specific computational models exhibit strong concordance with invasively measured fractional flow reserve (FFR)—the clinical gold standard for diagnosing coronary ischemia. However, current modeling techniques frequently rely on computationally intensive assumptions such as pulsatile flow dynamics and often fail to optimally leverage patient-specific clinical data that is routinely available, limiting their practical clinical adoption. In this…
