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[1] Bin Dong,et al. PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network , 2018, J. Comput. Phys..
[2] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[3] Maziar Raissi,et al. Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations , 2018, J. Mach. Learn. Res..
[4] Liu Yang,et al. Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations , 2018, SIAM J. Sci. Comput..
[5] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[6] Barak A. Pearlmutter,et al. Automatic differentiation in machine learning: a survey , 2015, J. Mach. Learn. Res..
[7] Paris Perdikaris,et al. Machine learning of linear differential equations using Gaussian processes , 2017, J. Comput. Phys..
[8] Hayden Schaeffer,et al. Learning Dynamical Systems and Bifurcation via Group Sparsity , 2017, 1709.01558.
[9] Linan Zhang,et al. On the Convergence of the SINDy Algorithm , 2018, Multiscale Model. Simul..
[10] Feliks Nüske,et al. Sparse learning of stochastic dynamical equations. , 2017, The Journal of chemical physics.
[11] Steven L. Brunton,et al. Methods for data-driven multiscale model discovery for materials , 2019, Journal of Physics: Materials.
[12] E Kaiser,et al. Sparse identification of nonlinear dynamics for model predictive control in the low-data limit , 2017, Proceedings of the Royal Society A.
[13] C. Basdevant,et al. Spectral and finite difference solutions of the Burgers equation , 1986 .
[14] J N Kutz,et al. Model selection for dynamical systems via sparse regression and information criteria , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[15] Hayden Schaeffer,et al. Extracting Sparse High-Dimensional Dynamics from Limited Data , 2017, SIAM J. Appl. Math..
[16] J. Carrera,et al. A stable computation of log‐derivatives from noisy drawdown data , 2017 .
[17] Steven L. Brunton,et al. Data-driven discovery of partial differential equations , 2016, Science Advances.
[18] Hayden Schaeffer,et al. Sparse model selection via integral terms. , 2017, Physical review. E.
[19] Steven L. Brunton,et al. Data-Driven Identification of Parametric Partial Differential Equations , 2018, SIAM J. Appl. Dyn. Syst..
[20] George Em Karniadakis,et al. Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems , 2018, J. Comput. Phys..
[21] Steven L. Brunton,et al. Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics , 2016, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.
[22] George Em Karniadakis,et al. fPINNs: Fractional Physics-Informed Neural Networks , 2018, SIAM J. Sci. Comput..
[23] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[24] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[25] George E. Karniadakis,et al. Hidden physics models: Machine learning of nonlinear partial differential equations , 2017, J. Comput. Phys..
[26] H. Schaeffer,et al. Learning partial differential equations via data discovery and sparse optimization , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[27] Kaj Nyström,et al. Data-driven discovery of PDEs in complex datasets , 2018, J. Comput. Phys..
[28] Bin Dong,et al. PDE-Net: Learning PDEs from Data , 2017, ICML.
[29] F. Jauberteau,et al. Numerical differentiation with noisy signal , 2009, Appl. Math. Comput..
[30] Haibin Chang,et al. Identification of physical processes via combined data-driven and data-assimilation methods , 2018, J. Comput. Phys..
[31] J. Cullum. Numerical Differentiation and Regularization , 1971 .
[32] Steven L. Brunton,et al. Data-driven discovery of coordinates and governing equations , 2019, Proceedings of the National Academy of Sciences.
[33] Haibin Chang,et al. Machine learning subsurface flow equations from data , 2019, Computational Geosciences.
[34] Rick Chartrand,et al. Numerical Differentiation of Noisy, Nonsmooth Data , 2011 .
[35] Steven L Brunton,et al. Sparse identification of nonlinear dynamics for rapid model recovery. , 2018, Chaos.
[36] Guang Lin,et al. Robust data-driven discovery of governing physical laws with error bars , 2018, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[37] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[38] Zhiping Mao,et al. DeepXDE: A Deep Learning Library for Solving Differential Equations , 2019, AAAI Spring Symposium: MLPS.