George Em Karniadakis

John Brown University — United States

Physics and Astronomy · Physical Sciences

9h-index628citations47works3.62yr avg
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Research Topics

Model Reduction and Neural Networks(16), Fluid Dynamics and Turbulent Flows(9), Neural Networks and Applications(8), Fluid Dynamics and Vibration Analysis(4), Machine Learning in Materials Science(3)

Publications47 total

State-space models are accurate and efficient neural operators for dynamical systems
Neural Networks·2025
Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
arXiv (Cornell University)·2025Open Access
Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics
arXiv (Cornell University)·2025Open Access
Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network
arXiv (Cornell University)·2025Open Access
Effect of Turbulence-Closure Consistency on Airfoil Identification
arXiv (Cornell University)·2025Open Access
AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
arXiv (Cornell University)·2025Open Access
Fusion-DeepONet: A data-efficient neural operator for geometry-dependent hypersonic and supersonic flows
Journal of Computational Physics·2025· 5 cited
Physics-Informed Machine Learning in Biomedical Science and Engineering
ArXiv.org·2025· 1 citedOpen Access
A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
arXiv (Cornell University)·2025Open Access
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
arXiv (Cornell University)·2025Open Access
A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning
2025· 1 citedOpen Access
FMEnets: Flow, material, and energy networks for non-ideal plug flow reactor design
Chemical Engineering Science·2025· 1 citedOpen Access
Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem
Computer Methods in Applied Mechanics and Engineering·2025
Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators (Adv. Mater. 29/2025)
Advanced Materials·2025· 4 citedOpen Access
Operator learning for reconstructing flow fields from sparse measurements: An energy transformer approach
Journal of Computational Physics·2025· 3 cited
DeepVIVONet: Using deep neural operators to optimize the location of sensors with application to vortex-induced vibrations
Journal of Fluids and Structures·2025· 1 cited
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
arXiv (Cornell University)·2025Open Access
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design
arXiv (Cornell University)·2025Open Access
Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators
Advanced Materials·2025· 12 citedOpen Access
Predicting crack nucleation and propagation in brittle materials using Deep Operator Networks with diverse trunk architectures
Computer Methods in Applied Mechanics and Engineering·2025· 15 cited
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Frequent Co-authors

Vivek Oommen(7), Elham Kiyani(6), Zheyuan Hu(6), Juan Diego Toscano(5), Ahmad Peyvan(5), Zongren Zou(4), Nazanin Ahmadi Daryakenari(4), Chenxi Wu(4), Qianying Cao(4), Aniruddha Bora(4), Kenji Kawaguchi(4), Khemraj Shukla(3), Additi Pandey(3), M. Manav(3), Nikhil Kadivar(3), Laura De Lorenzis(3), Alan John Varghese(3), Enrui Zhang(3), Qianli Shen(3), Adar Kahana(3)