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Machine learning-based modeling of physical systems has experienced increased interest in recent years.
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Data-driven reduced order modeling for time-dependent problems
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M. Guo and J. S. Hesthaven · 2018
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G. E. Karniadakis · 2018
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Deep Neural Networks motivated by Partial Differential Equations
L. Ruthotto and E. Haber · 2018
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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
S. L. Brunton and J. N. Kutz · 2019
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The M4 Competition: 100,000 time series and 61 forecasting methods
S. Makridakis, E. Spiliotis, and V. Assimakopoulos · 2019
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Dolfin-Adjoint 2018.1: Automated adjoints for FEniCS and Firedrake
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Pytorch: An imperative style, high-performance deep learning library
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N. Kovachki, Z. Li, B. Liu, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2021
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Characterizing possible failure modes in physics-informed neural networks
A. Krishnapriyan, A. Gholami, S. Zhe, R. Kirby, and M. W. Mahoney · 2021
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Simulation Intelligence: Towards a New Generation of Scientific Methods
A. Lavin, H. Zenil, B. Paige, D. Krakauer, J. Gottschlich, T. Mattson, A. Anandkumar, S. Choudry, K. Rocki, A. G. Baydin, C. Prunkl, B. Paige, O. Isayev, E. Peterson, P. L. McMahon, J. Macke, K. Cranmer, J. Zhang, H. Wainwright, A. Hanuka, M. Veloso, S. Assefa, S. Zheng, and A. Pfeffer · 2021
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Learning free-surface flow with physics-informed neural networks
R. Leiteritz, M. Hurler, and D. Pflüger · 2021
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Fourier neural operator for parametric partial differential equations
Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2021
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DeepXDE: A deep learning library for solving differential equations
L. Lu, X. Meng, Z. Mao, and G. E. Karniadakis · 2021
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Model Inversion for Spatio-temporal Processes using the Fourier Neural Operator
D. MacKinlay, D. Pagendam, P. M. Kuhnert, T. Cui, D. Robertson, and S. Janardhanan · 2021
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An extensible benchmark suite for learning to simulate physical systems
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When and why pinns fail to train: A neural tangent kernel perspective
S. Wang, X. Yu, and P. Perdikaris · 2021
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Physical design using differentiable learned simulators
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An overview of the hdf5 technology suite and its applications, 2022
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