Nicholas Zabaras

Decision Sciences · Social Sciences

52h-index10.1kcitations212works0.02yr avg
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Research Topics

Probabilistic and Robust Engineering Design(52), Model Reduction and Neural Networks(30), Metal Forming Simulation Techniques(29), Numerical methods in inverse problems(29), Metallurgy and Material Forming(27)

Publications212 total

Participants
2026
Inverse techniques for the design and control of solidification and forming processes
2026
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
Water Resources Research·2022· 32 citedOpen Access
Modern Deep Learning for Modeling Physical Systems
2022· 1 cited
Deep Learning for Modeling Multiscale Dynamical Systems
2022
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
Journal of Computational Physics·2022· 25 citedOpen Access
Transformers for modeling physical systems
Neural Networks·2021· 155 citedOpen Access
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties
2021
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
Journal of Mechanical Design·2021· 25 citedOpen Access
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
arXiv (Cornell University)·2021· 2 citedOpen Access
Transformers for Modeling Physical Systems
2021· 8 citedOpen Access
Transformers for Modeling Physical Systems
Zenodo (CERN European Organization for Nuclear Research)·2021Open Access
A Probabilistic Machine Learning Framework for Explicit Inverse Design of Industrial Gas Turbine Blades
2021· 1 cited
Solving inverse problems using conditional invertible neural networks
Journal of Computational Physics·2021· 74 citedOpen Access
Pro-ML IDeAS: A Probabilistic Framework for Explicit Inverse Design using Invertible Neural Network
AIAA Scitech 2021 Forum·2021· 7 cited
Multi-fidelity Generative Deep Learning Turbulent Flows
Zenodo (CERN European Organization for Nuclear Research)·2020Open Access
Multi-fidelity Generative Deep Learning Turbulent Flows
Zenodo (CERN European Organization for Nuclear Research)·2020Open Access
Multi-fidelity Generative Deep Learning Turbulent Flows
2020Open Access
Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoder
arXiv (Cornell University)·2020· 3 citedOpen Access
Embedded-physics machine learning for coarse-graining and collective variable discovery without data
arXiv (Cornell University)·2020· 1 citedOpen Access
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Frequent Co-authors

Baskar Ganapathysubramanian(4), Ilias Bilionis(4), Veera Sundararaghavan(4), Xiang Ma(3), Nicholas Geneva(3), Yinhao Zhu(2), Deborah Schnur(2), Jingbo Wang(2), Lijian Tan(2), Antoinette M. Maniatty(2), Swagato Acharjee(2), Subrata Mukherjee(2), Oliver P. Richmond(2), Yimin Ruan(2), George Z. Yang(2), Phaedon‐Stelios Koutsourelakis(1), Paris Perdikaris(1), Jingbo Wang(1), Bledar A. Konomi(1), Guang Lin(1)