Physics-informed neural networks for high-speed flows von Mao, Zhiping, Jagtap, Ameya D., Karniadakis, George Em

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Physics-informed neural networks (PINNs) for fluid mechanics: a review von Cai, Shengze, Mao, Zhiping, Wang, Zhicheng, Yin, Minglang, Karniadakis, George Em

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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators von Lu, Lu, Jin, Pengzhan, Pang, Guofei, Zhang, Zhongqiang, Karniadakis, George Em

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Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism von Li, Zhen, Bian, Xin, Li, Xiantao, Karniadakis, George Em

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One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization von Yin, Minglang, Yazdani, Alireza, Karniadakis, George Em

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A Petrov–Galerkin spectral element method for fractional elliptic problems von Kharazmi, Ehsan, Zayernouri, Mohsen, Karniadakis, George Em

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Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems von Kontolati, Katiana, Goswami, Somdatta, Em Karniadakis, George, Shields, Michael D.

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A comparative study of coarse-graining methods for polymeric fluids: Mori-Zwanzig vs. iterative Boltzmann inversion vs. stochastic parametric optimization von Li, Zhen, Bian, Xin, Yang, Xiu, Karniadakis, George Em

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations von Raissi, Maziar, Yazdani, Alireza, Karniadakis, George Em

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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems von Yu, Jeremy, Lu, Lu, Meng, Xuhui, Karniadakis, George Em

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How the spleen reshapes and retains young and old red blood cells: A computational investigation von Li, He, Liu, Zixiang Leonardo, Lu, Lu, Buffet, Pierre, Karniadakis, George Em

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Construction of non-Markovian coarse-grained models employing the Mori–Zwanzig formalism and iterative Boltzmann inversion von Yoshimoto, Yuta, Li, Zhen, Kinefuchi, Ikuya, Karniadakis, George Em

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Machine learning of linear differential equations using Gaussian processes von Raissi, Maziar, Perdikaris, Paris, Karniadakis, George Em

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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks von Jagtap, Ameya D., Kawaguchi, Kenji, Karniadakis, George Em

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Systems biology informed deep learning for inferring parameters and hidden dynamics von Yazdani, Alireza, Lu, Lu, Raissi, Maziar, Karniadakis, George Em

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Computational Biomechanics of Human Red Blood Cells in Hematological Disorders von Li, Xuejin, Li, He, Chang, Hung-Yu, Lykotrafitis, George, Em Karniadakis, George

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fPINNs: Fractional Physics-Informed Neural Networks von Pang, Guofei, Lu, Lu, Karniadakis, George Em

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Fractional spectral collocation methods for linear and nonlinear variable order FPDEs von Zayernouri, Mohsen, Karniadakis, George Em

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Physics-Informed Neural Networks for Heat Transfer Problems von Cai, Shengze, Wang, Zhicheng, Wang, Sifan, Perdikaris, Paris, Karniadakis, George Em

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Non-invasive inference of thrombus material properties with physics-informed neural networks von Yin, Minglang, Zheng, Xiaoning, Humphrey, Jay D., Karniadakis, George Em

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