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- Nicholas Zabaras

Discipline name
D-index
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Citations
Publications
World Ranking
National Ranking

Engineering and Technology
D-index
55
Citations
8,182
144
World Ranking
1014
National Ranking
449

- Statistics
- Artificial intelligence
- Thermodynamics

Algorithm, Stochastic process, Inverse problem, Mathematical optimization and Stochastic partial differential equation are his primary areas of study. His Algorithm study combines topics in areas such as Uncertainty quantification and Principal component analysis. His work deals with themes such as Randomness and Stochastic optimization, which intersect with Stochastic process.

His studies in Inverse problem integrate themes in fields like Mixed finite element method, Regularization, Inverse, Bayesian hierarchical modeling and Applied mathematics. His Stochastic partial differential equation study integrates concerns from other disciplines, such as Stochastic differential equation, Collocation and Sparse grid. His Polynomial study combines topics from a wide range of disciplines, such as Geometry and Finite element method.

- An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations (370 citations)
- Sparse grid collocation schemes for stochastic natural convection problems (343 citations)
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (205 citations)

Nicholas Zabaras focuses on Finite element method, Algorithm, Mathematical optimization, Uncertainty quantification and Inverse problem. His Finite element method research incorporates themes from Mechanics, Mathematical analysis and Geometry. Nicholas Zabaras combines subjects such as Artificial neural network, Bayesian probability, Bayesian inference, Probabilistic logic and Surrogate model with his study of Algorithm.

His Mathematical optimization research integrates issues from Kinematics, Stochastic process, Stochastic partial differential equation, Partial differential equation and Binary number. Nicholas Zabaras usually deals with Stochastic process and limits it to topics linked to Sparse grid and Collocation. Nicholas Zabaras works mostly in the field of Uncertainty quantification, limiting it down to topics relating to Artificial intelligence and, in certain cases, Pattern recognition, as a part of the same area of interest.

- Finite element method (27.72%)
- Algorithm (26.09%)
- Mathematical optimization (22.28%)

- Algorithm (26.09%)
- Uncertainty quantification (20.65%)
- Artificial intelligence (12.50%)

Nicholas Zabaras mainly investigates Algorithm, Uncertainty quantification, Artificial intelligence, Artificial neural network and Bayesian probability. His research in Algorithm intersects with topics in Inverse, Inverse problem and Surrogate model. His Uncertainty quantification research incorporates elements of Multiphase flow and Curse of dimensionality.

His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Complex system. His work on Bayesian inference as part of general Bayesian probability research is frequently linked to Gaussian random field, thereby connecting diverse disciplines of science. As a part of the same scientific study, he usually deals with the Deep learning, concentrating on Partial differential equation and frequently concerns with Numerical analysis, Mathematical optimization, Convolutional neural network, Field and Basis function.

- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (205 citations)
- Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification (187 citations)
- Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media (73 citations)

- Statistics
- Artificial intelligence
- Thermodynamics

Nicholas Zabaras mainly focuses on Algorithm, Uncertainty quantification, Artificial neural network, Artificial intelligence and Surrogate model. Nicholas Zabaras has included themes like Representation, Autoencoder and Inverse in his Algorithm study. His work carried out in the field of Uncertainty quantification brings together such families of science as Reynolds-averaged Navier–Stokes equations, Turbulence, Reynolds stress, Computational fluid dynamics and Applied mathematics.

His Artificial neural network research is multidisciplinary, incorporating perspectives in Data processing, Inverse problem, Leverage and Identification. His Deep learning, Bayesian inference and Complex system study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Trajectory, bridging the gap between disciplines. His Bayesian inference research is multidisciplinary, relying on both Probabilistic logic and Inference.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations

Xiang Ma;Nicholas Zabaras.

Journal of Computational Physics **(2009)**

535 Citations

Sparse grid collocation schemes for stochastic natural convection problems

Baskar Ganapathysubramanian;Nicholas Zabaras.

Journal of Computational Physics **(2007)**

499 Citations

An inverse method for determining elastic material properties and a material interface

D. S. Schnur;Nicholas Zabaras.

International Journal for Numerical Methods in Engineering **(1992)**

298 Citations

A Bayesian inference approach to the inverse heat conduction problem

Jingbo Wang;Nicholas Zabaras.

International Journal of Heat and Mass Transfer **(2004)**

240 Citations

An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations

Xiang Ma;Nicholas Zabaras.

Journal of Computational Physics **(2010)**

234 Citations

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

Yinhao Zhu;Nicholas Zabaras;Phaedon-Stelios Koutsourelakis;Paris Perdikaris.

Journal of Computational Physics **(2019)**

217 Citations

Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification

Yinhao Zhu;Nicholas Zabaras.

Journal of Computational Physics **(2018)**

207 Citations

Hierarchical Bayesian models for inverse problems in heat conduction

Jingbo Wang;Nicholas Zabaras.

Inverse Problems **(2005)**

185 Citations

Classification and reconstruction of three-dimensional microstructures using support vector machines

Veeraraghavan Sundararaghavan;Nicholas Zabaras.

Computational Materials Science **(2005)**

171 Citations

Using Bayesian statistics in the estimation of heat source in radiation

Jingbo Wang;Nicholas Zabaras.

International Journal of Heat and Mass Transfer **(2005)**

157 Citations

Cornell University

Nanjing University

University of Cambridge

University of Michigan–Ann Arbor

The Ohio State University

University of Minnesota

University of Pennsylvania

Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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