105 citations · 152 across the 16 of their papers we have counts for
5 papers · 1 filter
Assessing the performance of correlation-based multi-fidelity neural emulators
Cristian J. Villatoro, Gianluca Geraci, Daniele E. Schiavazzi
Outer loop tasks such as optimization, uncertainty quantification or inference can easily become intractable when the underlying high-fidelity model is computationally expensive. S…
Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study
Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein +3
Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-shows, variable arrival times, a…
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology
Mario De Florio, Zongren Zou, Daniele E. Schiavazzi +1
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model i…
InVAErt networks: a data-driven framework for model synthesis and identifiability analysis
Guoxiang Grayson Tong, Carlos A. Sing Long, Daniele E. Schiavazzi
Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable flexibility offered by data-driven arc…
Multifidelity data fusion in convolutional encoder/decoder networks
Lauren Partin, Gianluca Geraci, Ahmad Rushdi +2
We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. Besides requiring si…