1.1k citations · 3.6k across the 20 of their papers we have counts for
7 papers · 1 filter
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification
Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi +1
Identification of a groundwater contaminant source simultaneously with the hydraulic conductivity in highly-heterogeneous media often results in a high-dimensional inverse problem.…
Predictive Collective Variable Discovery with Deep Bayesian Models
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Extending spatio-temporal scale limitations of models for complex atomistic systems considered in biochemistry and materials science necessitates the development of enhanced sampli…
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
Steven Atkinson, Nicholas Zabaras
We introduce a methodology for nonlinear inverse problems using a variational Bayesian approach where the unknown quantity is a spatial field. A structured Bayesian Gaussian proces…
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Shaoxing Mo, Yinhao Zhu, Nicholas Zabaras +2
Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multip…
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
Nicholas Geneva, Nicholas Zabaras
Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics com…
Structured Bayesian Gaussian process latent variable model
Steven Atkinson, Nicholas Zabaras
We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-e…
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
Yinhao Zhu, Nicholas Zabaras
We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-d…