16 citations · 31 across the 13 of their papers we have counts for
4 papers · 1 filter
Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent Light Source
Ryan Humble, William Colocho, Finn O'Shea +2
Significant advances in utilizing deep learning for anomaly detection have been made in recent years. However, these methods largely assume the existence of a normal training set (…
Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks
Luca Pegolotti, Martin R. Pfaller, Natalia L. Rubio +4
Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience reduced accuracy when working with anatomies…
Physics-based parameterized neural ordinary differential equations: prediction of laser ignition in a rocket combustor
Yizhou Qian, Jonathan Wang, Quentin Douasbin +1
In this work, we present a novel physics-based data-driven framework for reduced-order modeling of laser ignition in a model rocket combustor based on parameterized neural ordinary…
Coincident Learning for Unsupervised Anomaly Detection
Ryan Humble, Zhe Zhang, Finn O'Shea +2
Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to…