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D-Index & Metrics

Engineering and Technology

D-Index
62
Citations
12764
World Ranking
1927
National Ranking
621

Overview

Nicholas Zabaras is affiliated with the University of Notre Dame in the United States and focuses on research in the field of Computer Science. Their work spans multiple subfields, including Statistical and Nonlinear Physics, Artificial Intelligence, Computational Theory and Mathematics, Environmental Engineering, and Materials Chemistry.

Their research addresses several main topics, such as Model Reduction and Neural Networks, Advanced Multi-Objective Optimization Algorithms, Groundwater Flow and Contamination Studies, Gaussian Processes and Bayesian Inference, Machine Learning in Materials Science, Protein Structure and Dynamics, and Water Systems and Optimization.

Zabaras has contributed publications to various academic venues. Frequent publication venues include:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Water Resources Research
  • Journal of Computational Physics
  • Neural Networks

Recent papers by Nicholas Zabaras cover topics in hydraulic conductivities, physical system modeling, inverse problem-solving, and deep learning approaches applied to environmental and computational physics problems. Notable recent publications include:

  • "Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non-Gaussian Hydraulic Conductivities" (2020, Water Resources Research)
  • "Transformers for modeling physical systems" (2021, Neural Networks)
  • "Solving inverse problems using conditional invertible neural networks" (2021, Journal of Computational Physics)
  • "Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties" (2022, Water Resources Research)
  • "Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems" (2022, Journal of Computational Physics)

Zabaras frequently collaborates with several researchers across their publications. Common co-authors include:

  • Nicholas Geneva
  • Govinda Anantha Padmanabha
  • Cheng Peng
  • Valeria Andreoli
  • Steven Atkinson

Best Publications

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

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

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

    Yinhao Zhu;Nicholas Zabaras

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

    Xiang Ma;Nicholas Zabaras

  • Sparse grid collocation schemes for stochastic natural convection problems

    Baskar Ganapathysubramanian;Nicholas Zabaras

  • A Bayesian inference approach to the inverse heat conduction problem

    Jingbo Wang;Nicholas Zabaras

  • Deep Convolutional Encoder-Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media

    Shaoxing Mo;Shaoxing Mo;Yinhao Zhu;Nicholas Zabaras;Xiaoqing Shi

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

    D. S. Schnur;Nicholas Zabaras

  • Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

    Nicholas Geneva;Nicholas Zabaras

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

    Xiang Ma;Nicholas Zabaras

  • Hierarchical Bayesian models for inverse problems in heat conduction

    Jingbo Wang;Nicholas Zabaras

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

    Veeraraghavan Sundararaghavan;Nicholas Zabaras

  • Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

    Ilias Bilionis;Nicholas Zabaras;Bledar A. Konomi;Guang Lin

  • Multi-output local Gaussian process regression: Applications to uncertainty quantification

    Ilias Bilionis;Nicholas Zabaras

  • An efficient Bayesian inference approach to inverse problems based on an adaptive sparse grid collocation method

    Xiang Ma;Nicholas Zabaras

  • Using Bayesian statistics in the estimation of heat source in radiation

    Jingbo Wang;Nicholas Zabaras

  • A level set simulation of dendritic solidification with combined features of front-tracking and fixed-domain methods

    Lijian Tan;Nicholas Zabaras

  • Transformers for Modeling Physical Systems

    Nicholas Geneva;Nicholas Zabaras

  • Finite Element Analysis of Some Inverse Elasticity Problems

    Antoinette Maniatty;Nicholas Zabaras;Kim Stelson

  • Finite element solution of two‐dimensional inverse elastic problems using spatial smoothing

    D. S. Schnur;Nicholas Zabaras

  • A sensitivity analysis for the optimal design of metal-forming processes

    S. Badrinarayanan;Nicholas Zabaras

  • Deep Autoregressive Neural Networks for High-Dimensional Inverse Problems in Groundwater Contaminant Source Identification

    Shaoxing Mo;Shaoxing Mo;Nicholas Zabaras;Xiaoqing Shi;Jichun Wu

Frequent Co-Authors

Baskar Ganapathysubramanian
Baskar Ganapathysubramanian Iowa State University
Subrata Mukherjee
Subrata Mukherjee Cornell University
Jichun Wu
Jichun Wu Nanjing University
Abul Fazal M. Arif
Abul Fazal M. Arif McMaster University
Guang Lin
Guang Lin Purdue University West Lafayette
Mark Girolami
Mark Girolami University of Cambridge
Paris Perdikaris
Paris Perdikaris University of Pennsylvania
Dongbin Xiu
Dongbin Xiu The Ohio State University
Shenyang Y. Hu
Shenyang Y. Hu Pacific Northwest National Laboratory
Fei Gao
Fei Gao University of Michigan–Ann Arbor

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