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Modeling real-world spatio-temporal data is exceptionally difficult due to inherent high dimensionality, measurement noise, partial observations, and often expensive data collection procedures.
Hamiltonian systems and transformation in hilbert space
Bernard O Koopman · 1931
Earlier work this paper cites.
Dynamical systems of continuous spectra
Bernard O Koopman and J v Neumann · 1932
Earlier work this paper cites.
Computer methods for mathematical computation prentice-hall
George E Forsythe, Michal A Malcolm, and Cleve B Moler · 1977
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
Earlier work this paper cites.
Weak convergence
Aad W Van Der Vaart, Jon A Wellner, Aad W van der Vaart, and Jon A Wellner · 1996
Earlier work this paper cites.
Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation
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Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Probability in Banach Spaces: isoperimetry and processes
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High dimensional thresholded regression and shrinkage effect
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum · 2015
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Flow visualization in a water channel, 2017
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Samuel H Rudy, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2017
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Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and Philip S Yu · 2017
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Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, and Josh Tenenbaum · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Size-independent sample complexity of neural networks
Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2018
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Visualizing the loss landscape of neural nets
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Pde-net: Learning pdes from data
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Deep learning for universal linear embeddings of nonlinear dynamics
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Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F Fei-Fei, Josh Tenenbaum, and Daniel L Yamins · 2018
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Graph networks as learnable physics engines for inference and control
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
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Zhangyang Gao, Cheng Tan, Lirong Wu, and Stan Z Li · 2022
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Self-supervised learning from images with a joint-embedding predictive architecture
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Deep learning for physical processes: Incorporating prior scientific knowledge
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Linearly recurrent autoencoder networks for learning dynamics
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On the spectral bias of neural networks
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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High-dimensional statistics: A non-asymptotic viewpoint
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Flat minima generalize for low-rank matrix recovery
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Shallow recurrent decoder for reduced order modeling of plasma dynamics
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Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
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Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
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Deep Learning for Partially Observed Dynamical Systems
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Stability analysis of chaotic systems in latent spaces
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β \beta -variational autoencoders and transformers for reduced-order modelling of fluid flows
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Sensing with shallow recurrent decoder networks
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