16 citations · 31 across the 13 of their papers we have counts for
14 papers
Learning to Bias: Machine Learning-Enhanced Particle Filters
Apoorv Srivastava, Eric Darve
Sequential inference estimates latent states from noisy and incomplete observations. Particle Filters (PFs), a class of Monte Carlo methods based on importance sampling, provide a…
Data-Driven Bifurcation Handling in Physics-Based Reduced-Order Vascular Hemodynamic Models
Natalia L. Rubio, Eric F. Darve, Alison L. Marsden
Three-dimensional (3D) finite-element simulations of cardiovascular flows provide high-fidelity predictions to support cardiovascular medicine, but their high computational cost li…
Multi-fidelity Hamiltonian Monte Carlo
Dhruv V. Patel, Jonghyun Lee, Matthew W. Farthing +2
Numerous applications in biology, statistics, science, and engineering require generating samples from high-dimensional probability distributions. In recent years, the Hamiltonian…
Hybrid Physics-Based and Data-Driven Modeling of Vascular Bifurcation Pressure Differences
Natalia L. Rubio, Luca Pegolotti, Martin R. Pfaller +2
Reduced-order models (ROMs) allow for the simulation of blood flow in patient-specific vasculatures without the high computational cost and wait time associated with traditional co…
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…
Beam-based RF Station Fault Identification at the SLAC Linac Coherent Light Source
Ryan Humble, Finn H. O'Shea, William Colocho +4
Accelerators produce too many signals for a small operations team to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with…
Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land s…
Deep learning-based fast solver of the shallow water equations
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Fast and reliable prediction of river flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for t…
Autonomous Inversion of In Situ Deformation Measurement Data for CO2 Storage Decision Support
Jeff Burghardt, Ting Bao, Kailai Xu +2
Current methods of estimating the change in stress caused by injecting fluid into subsurface formations require choosing the type of constitutive model and the model parameters bas…
Application of deep learning to large scale riverine flow velocity estimation
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Fast and reliable prediction of riverine flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used fo…
Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry
Yizhou Qian, Mojtaba Forghani, Jonghyun Harry Lee +4
Nearshore bathymetry, the topography of the ocean floor in coastal zones, is vital for predicting the surf zone hydrodynamics and for route planning to avoid subsurface features. H…