Search Results - "Journal of computational physics" :: K.UTB vyhledávací portál

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When and why PINNs fail to train: A neural tangent kernel perspective

by Wang, Sifan, Yu, Xinling, Perdikaris, Paris
Published in Journal of computational physics (15.01.2022)

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NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

by Jin, Xiaowei, Cai, Shengze, Li, Hui, Karniadakis, George Em
Published in Journal of computational physics (01.02.2021)

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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

by Raissi, M., Perdikaris, P., Karniadakis, G.E.
Published in Journal of computational physics (01.02.2019)

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

by Yang, Liu, Meng, Xuhui, Karniadakis, George Em
Published in Journal of computational physics (15.01.2021)

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DGM: A deep learning algorithm for solving partial differential equations

by Sirignano, Justin, Spiliopoulos, Konstantinos
Published in Journal of computational physics (15.12.2018)

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

by Zhu, Yinhao, Zabaras, Nicholas, Koutsourelakis, Phaedon-Stelios, Perdikaris, Paris
Published in Journal of computational physics (01.10.2019)

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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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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

by Lee, Kookjin, Carlberg, Kevin T.
Published in Journal of computational physics (01.03.2020)

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PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

by Gao, Han, Sun, Luning, Wang, Jian-Xun
Published in Journal of computational physics (01.03.2021)

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PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

by Long, Zichao, Lu, Yiping, Dong, Bin
Published in Journal of computational physics (15.12.2019)

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Hidden physics models: Machine learning of nonlinear partial differential equations

by Raissi, Maziar, Karniadakis, George Em
Published in Journal of computational physics (15.03.2018)

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A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

by Meng, Xuhui, Karniadakis, George Em
Published in Journal of computational physics (15.01.2020)

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Weak adversarial networks for high-dimensional partial differential equations

by Zang, Yaohua, Bao, Gang, Ye, Xiaojing, Zhou, Haomin
Published in Journal of computational physics (15.06.2020)

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The scalar auxiliary variable (SAV) approach for gradient flows

by Shen, Jie, Xu, Jie, Yang, Jiang
Published in Journal of computational physics (15.01.2018)

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Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification

by Zhu, Yinhao, Zabaras, Nicholas
Published in Journal of computational physics (01.08.2018)

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

by Zhang, Dongkun, Lu, Lu, Guo, Ling, Karniadakis, George Em
Published in Journal of computational physics (15.11.2019)

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$What is the fractional Laplacian? A comparative review with new results$

What is the fractional Laplacian? A comparative review with new results

by Lischke, Anna, Pang, Guofei, Gulian, Mamikon, Song, Fangying, Glusa, Christian, Zheng, Xiaoning, Mao, Zhiping, Cai, Wei, Meerschaert, Mark M., Ainsworth, Mark, Karniadakis, George Em
Published in Journal of computational physics (01.03.2020)

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Adversarial uncertainty quantification in physics-informed neural networks

by Yang, Yibo, Perdikaris, Paris
Published in Journal of computational physics (01.10.2019)

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A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations

by Yuan, Lei, Ni, Yi-Qing, Deng, Xiang-Yun, Hao, Shuo
Published in Journal of computational physics (01.08.2022)

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Non-intrusive reduced order modeling of nonlinear problems using neural networks

by Hesthaven, J.S., Ubbiali, S.
Published in Journal of computational physics (15.06.2018)

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