100 citations · 107 across the 6 of their papers we have counts for
6 papers
Quasi-Monte Carlo methods for uncertainty quantification of tumor growth modeled by a parametric semi-linear parabolic reaction-diffusion equation
Alexander D. Gilbert, Frances Y. Kuo, Dirk Nuyens +3
We study the application of a quasi-Monte Carlo (QMC) method to a class of semi-linear parabolic reaction-diffusion partial differential equations used to model tumor growth. Mathe…
Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology
Graham Pash, Umberto Villa, David A. Hormuth +2
Quantifying the uncertainty in predictive models is critical for establishing trust and enabling risk-informed decision making for personalized medicine. In contrast to one-size-fi…
TumorTwin: A python framework for patient-specific digital twins in oncology
Michael Kapteyn, Anirban Chaudhuri, Ernesto A. B. F. Lima +5
Background: Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and treatment response on a patient-s…
Validating the predictions of mathematical models describing tumor growth and treatment response
Guillermo Lorenzo, David A. Hormuth, Chengyue Wu +7
Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticip…
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
Graham Pash, Malik Hassanaly, Shashank Yellapantula
While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical s…
Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas
Anirban Chaudhuri, Graham Pash, David A. Hormuth +6
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipa…