Search Results - "George, EM" :: K.UTB vyhledávací portál

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

by Raissi, Maziar, Yazdani, Alireza, Karniadakis, George Em
Published in Science (American Association for the Advancement of Science) (28.02.2020)

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Physics-informed neural networks for high-speed flows

by Mao, Zhiping, Jagtap, Ameya D., Karniadakis, George Em
Published in Computer methods in applied mechanics and engineering (01.03.2020)

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Machine learning of linear differential equations using Gaussian processes

by Raissi, Maziar, Perdikaris, Paris, Karniadakis, George Em
Published in Journal of computational physics (01.11.2017)

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

by Jagtap, Ameya D., Kawaguchi, Kenji, Karniadakis, George Em
Published in Journal of computational physics (01.03.2020)

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Physics-informed neural networks (PINNs) for fluid mechanics: a review

by Cai, Shengze, Mao, Zhiping, Wang, Zhicheng, Yin, Minglang, Karniadakis, George Em
Published in Acta mechanica Sinica (01.12.2021)

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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

by Lu, Lu, Jin, Pengzhan, Pang, Guofei, Zhang, Zhongqiang, Karniadakis, George Em
Published in Nature machine intelligence (01.03.2021)

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Inferring solutions of differential equations using noisy multi-fidelity data

by Raissi, Maziar, Perdikaris, Paris, Karniadakis, George Em
Published in Journal of computational physics (15.04.2017)

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Operator learning for predicting multiscale bubble growth dynamics

by Lin, Chensen, Li, Zhen, Lu, Lu, Cai, Shengze, Maxey, Martin, Karniadakis, George Em
Published in The Journal of chemical physics (14.03.2021)

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Systems biology informed deep learning for inferring parameters and hidden dynamics

by Yazdani, Alireza, Lu, Lu, Raissi, Maziar, Karniadakis, George Em
Published in PLoS computational biology (18.11.2020)

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Physics-informed neural networks for inverse problems in nano-optics and metamaterials

by Chen, Yuyao, Lu, Lu, Karniadakis, George Em, Dal Negro, Luca
Published in Optics express (13.04.2020)

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Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

by Cai, Shengze, Wang, Zhicheng, Fuest, Frederik, Jeon, Young Jin, Gray, Callum, Karniadakis, George Em
Published in Journal of fluid mechanics (25.03.2021)

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Deep learning of vortex-induced vibrations

by Raissi, Maziar, Wang, Zhicheng, Triantafyllou, Michael S., Karniadakis, George Em
Published in Journal of fluid mechanics (25.02.2019)

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Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

by Li, Zhen, Bian, Xin, Li, Xiantao, Karniadakis, George Em
Published in The Journal of chemical physics (28.12.2015)

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Reinforcement learning for bluff body active flow control in experiments and simulations

by Fan, Dixia, Yang, Liu, Wang, Zhicheng, Triantafyllou, Michael S., Karniadakis, George Em
Published in Proceedings of the National Academy of Sciences - PNAS (20.10.2020)

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$A Petrov–Galerkin spectral element method for fractional elliptic problems$

A Petrov–Galerkin spectral element method for fractional elliptic problems

by Kharazmi, Ehsan, Zayernouri, Mohsen, Karniadakis, George Em
Published in Computer methods in applied mechanics and engineering (01.09.2017)

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Computing the non-Markovian coarse-grained interactions derived from the Mori-Zwanzig formalism in molecular systems: Application to polymer melts

by Li, Zhen, Lee, Hee Sun, Darve, Eric, Karniadakis, George Em
Published in The Journal of chemical physics (07.01.2017)

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nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

by Mahmoudabadbozchelou, Mohammadamin, Karniadakis, George Em, Jamali, Safa
Published in Soft matter (22.12.2021)

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Fractional-Order Viscoelasticity in One-Dimensional Blood Flow Models

by Perdikaris, Paris, Karniadakis, George Em
Published in Annals of biomedical engineering (01.05.2014)

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$One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization$

One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization

by Yin, Minglang, Yazdani, Alireza, Karniadakis, George Em
Published in Computer methods in applied mechanics and engineering (15.08.2019)

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Physics-Informed Neural Network for Ultrasound Nondestructive Quantification of Surface Breaking Cracks

by Shukla, Khemraj, Di Leoni, Patricio Clark, Blackshire, James, Sparkman, Daniel, Karniadakis, George Em
Published in Journal of nondestructive evaluation (2020)

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