George Em Karniadakis Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

John Brown University

faculty

9 h-index 47 pubs 614 cited

Research Areas

Model Reduction and Neural Networks Fluid Dynamics and Vibration Analysis Fluid Dynamics and Turbulent Flows Machine Learning in Materials Science Neural Networks and Applications Climate variability and models

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OverviewAI-generated summary

George Em Karniadakis's research encompasses a diverse range of topics, including fluid dynamics, vibration analysis, and the application of neural networks to various scientific and engineering problems. He is particularly active in the realm of physics-informed machine learning, as evidenced by recent work on topics ranging from uncertainty quantification in neural networks to hybrid frameworks coupling PINNs (physics-informed neural networks) with spectral elements for simulating multiphysics problems. Karniadakis's investigations extend to materials science, including the characterization and inverse design of stochastic mechanical metamaterials using neural operators, and the application of physics-informed machine learning to solar-thermal power systems.

His primary research focus involves model reduction, neural networks, and the integration of machine learning techniques into materials science, climate modeling, and fluid dynamics.

Metrics

  • h-index: 9
  • Publications: 47
  • Citations: 614

Selected Publications

  • Code and Data for "Learning Turbulent Flows with Generative Models for Super Resolution and Sparse Flow Reconstruction" (2025) DOI
  • Code and Data for "Learning Turbulent Flows with Generative Models for Super Resolution and Sparse Flow Reconstruction" (2025) DOI
  • Crack Path Prediction with Operator Learning Using Discrete Particle System Data Generation (2025) DOI
  • Operator learning for reconstructing flow fields from sparse measurements: An energy transformer approach (2025) DOI
  • Deepomamba: State-Space Model for Spatio-Temporal Pde Neural Operator Learning (2025) DOI
  • Automatic Discovery of Optimal Meta-Solvers Via Multi-Objective Optimization (2025) DOI
  • Predicting Crack Nucleation and Propagation in Brittle Materials Using Deep Operator Networks with Diverse Trunk Architectures (2024) DOI
  • Multi-Head Physics-Informed Neural Networks for Learning Functional Priors and Uncertainty Quantification (2024) DOI
  • State-Space Models are Accurate and Efficient Neural Operators for Dynamical Systems (2024) DOI
  • Sympgnns: Symplectic Graph Neural Networks for Identifying High-Dimensional Hamiltonian Systems and Node Classification (2024) DOI
  • Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification (2024) DOI

Collaborators

Researchers in the database who share publications

Vivek Oommen John Brown University 7 shared publications Juan Diego Toscano John Brown University 5 shared publications Aniruddha Bora John Brown University 4 shared publications Nazanin Ahmadi Daryakenari John Brown University 4 shared publications Qianying Cao John Brown University 4 shared publications Alan John Varghese John Brown University 3 shared publications Ameya D. Jagtap John Brown University 2 shared publications Melih Ağraz John Brown University 2 shared publications

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