270 citations · 1.5k across the 27 of their papers we have counts for
5 papers · 1 filter
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…
Learning physics-based reduced-order models from data using nonlinear manifolds
Rudy Geelen, Laura Balzano, Stephen Wright +1
We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the d…
A digital twin framework for civil engineering structures
Matteo Torzoni, Marco Tezzele, Stefano Mariani +2
The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced li…
Learning latent representations in high-dimensional state spaces using polynomial manifold constructions
Rudy Geelen, Laura Balzano, Karen Willcox
We present a novel framework for learning cost-efficient latent representations in problems with high-dimensional state spaces through nonlinear dimension reduction. By enriching l…
Multi-output multilevel best linear unbiased estimators via semidefinite programming
M. Croci, K. E. Willcox, S. J. Wright
Multifidelity forward uncertainty quantification (UQ) problems often involve multiple quantities of interest and heterogeneous models (e.g., different grids, equations, dimensions,…