What is he best known for?
The fields of study he is best known for:
- 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.
His most cited work include:
- 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)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Finite element method (27.72%)
- Algorithm (26.09%)
- Mathematical optimization (22.28%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (26.09%)
- Uncertainty quantification (20.65%)
- Artificial intelligence (12.50%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- 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)
In his most recent research, the most cited papers focused on:
- 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.
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