George Em Karniadakis - researchr alias

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Viewing Publication 1 - 59 from 59

2024

2023

Instability-wave prediction in hypersonic boundary layers with physics-informed neural operatorsYue Hao, Patricio Clark Di Leoni, Olaf Marxen, Charles Meneveau, George Em Karniadakis, Tamer A. Zaki. jocs, 73:102120, November 2023. [doi]

A hybrid deep neural operator/finite element method for ice-sheet modelingQizhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis. jcphy, 492:112428, November 2023. [doi]

Multifidelity deep operator networks for data-driven and physics-informed problemsAmanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis. jcphy, 493:112462, November 2023. [doi]

Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural NetworksPengzhan Jin, Zhen Zhang 0029, Ioannis G. Kevrekidis, George Em Karniadakis. tnn, 34(11):8271-8283, November 2023. [doi]

On the influence of over-parameterization in manifold based surrogates and deep neural operatorsKatiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis. jcphy, 479:112008, April 2023. [doi]

Neural operator prediction of linear instability waves in high-speed boundary layersPatricio Clark Di Leoni, Lu Lu 0010, Charles Meneveau, George Em Karniadakis, Tamer A. Zaki. jcphy, 474:111793, February 2023. [doi]

A combined computational and experimental investigation of the filtration function of splenic macrophages in sickle cell diseaseGuansheng Li, Yuhao Qiang, He Li, Xuejin Li, Pierre Buffet, Ming Dao, George Em Karniadakis. ploscb, 19(12), December 2023. [doi]

A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositionsMichael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby. jcphy, 493:112464, November 2023. [doi]

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisonsApostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis. jcphy, 477:111902, March 2023. [doi]

Accelerating gradient descent and Adam via fractional gradientsYeonjong Shin, Jérôme Darbon, George Em Karniadakis. NN, 161:185-201, April 2023. [doi]

2022

Approximation rates of DeepONets for learning operators arising from advection-diffusion equationsBeichuan Deng, Yeonjong Shin, Lu Lu 0010, Zhongqiang Zhang, George Em Karniadakis. NN, 153:411-426, 2022. [doi]

Deep transfer operator learning for partial differential equations under conditional shiftSomdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis. natmi, 4(12):1155-1164, December 2022. [doi]

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi. siamsc, 44(5):3158, 2022. [doi]

Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functionsAmeya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis. ijon, 468:165-180, 2022. [doi]

Physics-informed neural networks for inverse problems in supersonic flowsAmeya D. Jagtap, Zhiping Mao, Nikolaus Adams, George Em Karniadakis. jcphy, 466:111402, 2022. [doi]

Learning functional priors and posteriors from data and physicsXuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis. jcphy, 457:111073, 2022. [doi]

Discovering and forecasting extreme events via active learning in neural operatorsEthan Pickering, Stephen Guth, George Em Karniadakis, Themistoklis P. Sapsis. ncs, 2(12):823-833, 2022. [doi]

Meta-learning PINN loss functionsApostolos F. Psaros, Kenji Kawaguchi, George Em Karniadakis. jcphy, 458:111121, 2022. [doi]

A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problemsKhemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, George Em Karniadakis. spm, 39(1):68-77, 2022. [doi]

2 Optimal Transport Regularity for Generative ModelsLiu Yang, George Em Karniadakis. tnn, 33(2):528-538, 2022. [doi]

G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learningEnrui Zhang, Bart Spronck, Jay D. Humphrey, George Em Karniadakis. ploscb, 18(10):1010660, October 2022. [doi]

A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decompositionYue Zhao, Zhiping Mao, Ling Guo, Yifa Tang, George Em Karniadakis. jcphy, 461:111213, 2022. [doi]

2021

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operatorsLu Lu 0010, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis. natmi, 3(3):218-229, 2021. [doi]

DeepXDE: A Deep Learning Library for Solving Differential EquationsLu Lu 0010, Xuhui Meng, Zhiping Mao, George Em Karniadakis. siamrev, 63(1):208-228, 2021. [doi]

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networksShengze Cai, Zhicheng Wang, Lu Lu 0010, Tamer A. Zaki, George Em Karniadakis. jcphy, 436:110296, 2021. [doi]

An open-source parallel code for computing the spectral fractional Laplacian on 3D complex geometry domainsMax Carlson, Xiaoning Zheng, Hari Sundar, George Em Karniadakis, Robert M. Kirby. cphysics, 261:107695, 2021. [doi]

Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-Informed Neural NetworksXiaoli Chen, Liu Yang, Jinqiao Duan, George Em Karniadakis. siamsc, 43(3), 2021. [doi]

Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patientsYixiang Deng, Lu Lu 0010, Laura Aponte, Angeliki M. Angelidi, Vera Novak, George Em Karniadakis, Christos S. Mantzoros. npjdm, 4, 2021. [doi]

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equationsXiaowei Jin, Shengze Cai, Hui Li 0035, George Em Karniadakis. jcphy, 426:109951, 2021. [doi]

Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networksEhsan Kharazmi, Min Cai, Xiaoning Zheng, Zhen Zhang 0029, Guang Lin, George Em Karniadakis. ncs, 1(11):744-753, 2021. [doi]

How the spleen reshapes and retains young and old red blood cells: A computational investigationHe Li, Zixiang Leonardo Liu, Lu Lu 0010, Pierre Buffet, George Em Karniadakis. ploscb, 17(11), 2021. [doi]

Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulationQin Lou, Xuhui Meng, George Em Karniadakis. jcphy, 447:110676, 2021. [doi]

DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operatorsZhiping Mao, Lu Lu 0010, Olaf Marxen, Tamer A. Zaki, George Em Karniadakis. jcphy, 447:110698, 2021. [doi]

Multi-fidelity Bayesian neural networks: Algorithms and applicationsXuhui Meng, Hessam Babaee, George Em Karniadakis. jcphy, 438:110361, 2021. [doi]

Parallel physics-informed neural networks via domain decompositionKhemraj Shukla, Ameya D. Jagtap, George Em Karniadakis. jcphy, 447:110683, 2021. [doi]

Variable-Order Fractional Models for Wall-Bounded Turbulent FlowsFangying Song, George Em Karniadakis. entropy, 23(6):782, 2021. [doi]

A phase-field method for boiling heat transferZhicheng Wang, Xiaoning Zheng, Chryssostomos Chryssostomidis, George Em Karniadakis. jcphy, 435:110239, 2021. [doi]

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy dataLiu Yang, Xuhui Meng, George Em Karniadakis. jcphy, 425:109913, 2021. [doi]

Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flowsLifei Zhao, Zhen Li 0003, Zhicheng Wang, Bruce Caswell, Jie Ouyang, George Em Karniadakis. jcphy, 427:110069, 2021. [doi]

2020

DeepXDE: A Deep Learning Library for Solving Differential EquationsLu Lu 0010, Xuhui Meng, Zhiping Mao, George Em Karniadakis. aaaiss 2020: [doi]

Adaptive activation functions accelerate convergence in deep and physics-informed neural networksAmeya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis. jcphy, 404, 2020. [doi]

Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothnessPengzhan Jin, Lu Lu 0010, Yifa Tang, George Em Karniadakis. NN, 130:85-99, 2020. [doi]

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systemsPengzhan Jin, Zhen Zhang, Aiqing Zhu, Yifa Tang, George Em Karniadakis. NN, 132:166-179, 2020. [doi]

What is the fractional Laplacian? A comparative review with new resultsAnna Lischke, Guofei Pang, Mamikon A. Gulian, Fangying Song, Christian Glusa, Xiaoning Zheng, Zhiping Mao, Wei Cai 0003, Mark M. Meerschaert, Mark Ainsworth, George Em Karniadakis. jcphy, 404, 2020. [doi]

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problemsXuhui Meng, George Em Karniadakis. jcphy, 401, 2020. [doi]

Physics-Informed Generative Adversarial Networks for Stochastic Differential EquationsLiu Yang, Dongkun Zhang, George Em Karniadakis. siamsc, 42(1), 2020. [doi]

Systems biology informed deep learning for inferring parameters and hidden dynamicsAlireza Yazdani, Lu Lu 0010, Maziar Raissi, George Em Karniadakis. ploscb, 16(11), 2020. [doi]

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural NetworksDongkun Zhang, Ling Guo, George Em Karniadakis. siamsc, 42(2), 2020. [doi]

A stabilized semi-implicit Fourier spectral method for nonlinear space-fractional reaction-diffusion equationsHui Zhang, Xiaoyun Jiang, Fanhai Zeng, George Em Karniadakis. jcphy, 405:109141, 2020. [doi]

Physics-informed semantic inpainting: Application to geostatistical modelingQiang Zheng, Lingzao Zeng, George Em Karniadakis. jcphy, 419:109676, 2020. [doi]

2019

2012

Multiscale simulations of blood-flow: from a platelet to an arteryLeopold Grinberg, Mingge Deng, Huan Lei, Joseph A. Insley, George Em Karniadakis. xsede 2012: [doi]

1997

hpDevelopment of a Parallel Unstructured Spectral/ Method for Unsteady Fluid DynamicsSpencer J. Sherwin, Constantinos Evangelinos, H. Tufo, George Em Karniadakis. pcfd 1998: 273-280 [doi]

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Publications by 'George Em Karniadakis'

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2023

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2020

2019

2012

1997

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