1.1k citations · 3.6k across the 20 of their papers we have counts for
21 papers
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
Zitong Zhou, Nicholas Zabaras, Daniel M. Tartakovsky
Identifying the heterogeneous conductivity field and reconstructing the contaminant release history are key aspects of subsurface remediation. Achieving these two goals with limite…
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
Sayan Ghosh, Govinda A. Padmanabha, Cheng Peng +6
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durab…
A Bayesian Multiscale Deep Learning Framework for Flows in Random Media
Govinda Anantha Padmanabha, Nicholas Zabaras
Fine-scale simulation of complex systems governed by multiscale partial differential equations (PDEs) is computationally expensive and various multiscale methods have been develope…
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
Yingzhi Xia, Nicholas Zabaras
Estimation of spatially-varying parameters for computationally expensive forward models governed by partial differential equations is addressed. A novel multiscale Bayesian inferen…
Transformers for Modeling Physical Systems
Nicholas Geneva, Nicholas Zabaras
Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performan…
Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
Navid Shervani-Tabar, Nicholas Zabaras
Recent advances in artificial intelligence have propelled the development of innovative computational materials modeling and design techniques. Generative deep learning models have…
Solving inverse problems using conditional invertible neural networks
Govinda Anantha Padmanabha, Nicholas Zabaras
Inverse modeling for computing a high-dimensional spatially-varying property field from indirect sparse and noisy observations is a challenging problem. This is due to the complex…
Multi-fidelity Generative Deep Learning Turbulent Flows
Nicholas Geneva, Nicholas Zabaras
In computational fluid dynamics, there is an inevitable trade off between accuracy and computational cost. In this work, a novel multi-fidelity deep generative model is introduced…
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in th…
Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities
Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi +1
Inverse modeling for the estimation of non-Gaussian hydraulic conductivity fields in subsurface flow and solute transport models remains a challenging problem. This is mainly due t…
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks
Nicholas Geneva, Nicholas Zabaras
In recent years, deep learning has proven to be a viable methodology for surrogate modeling and uncertainty quantification for a vast number of physical systems. However, in their…
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis +1
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for tra…
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…
Predictive Coarse-Graining
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse ma…
Variational Reformulation of Bayesian Inverse Problems
Panagiotis Tsilifis, Ilias Bilionis, Ioannis Katsounaros +1
The classical approach to inverse problems is based on the optimization of a misfit function. Despite its computational appeal, such an approach suffers from many shortcomings, e.g…