4 citations · 7 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…
Projection-based multifidelity linear regression for data-scarce applications
Vignesh Sella, Julie Pham, Karen Willcox +1
Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifide…
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…
Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems
Shane A. McQuarrie, Anirban Chaudhuri, Karen E. Willcox +1
This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use…