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Kamyar Azizzadenesheli | Anima Anandkumar | Andrew Stuart | Zongyi Li | Nikola Kovachki | Burigede Liu | Kaushik Bhattacharya | Nikola B. Kovachki | K. Azizzadenesheli | Anima Anandkumar | Zong-Yi Li | Burigede Liu | K. Bhattacharya | Andrew Stuart | Andrew M. Stuart
[1] E. Nyström. Über Die Praktische Auflösung von Integralgleichungen mit Anwendungen auf Randwertaufgaben , 1930 .
[2] D. Gilbarg,et al. Elliptic Partial Differential Equa-tions of Second Order , 1977 .
[3] M. Gurtin,et al. An introduction to continuum mechanics , 1981 .
[4] Jacob Bear,et al. Fundamentals of transport phenomena in porous media , 1984 .
[5] Claes Johnson. Numerical solution of partial differential equations by the finite element method , 1988 .
[6] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[7] Christopher K. I. Williams. Computing with Infinite Networks , 1996, NIPS.
[8] P. Bassanini,et al. Elliptic Partial Differential Equations of Second Order , 1997 .
[9] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[10] Jitendra Malik,et al. Spectral Partitioning with Indefinite Kernels Using the Nyström Extension , 2002, ECCV.
[11] Christopher K. I. Williams,et al. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .
[12] Nicolas Le Roux,et al. Continuous Neural Networks , 2007, AISTATS.
[13] Mikhail Belkin,et al. On Learning with Integral Operators , 2010, J. Mach. Learn. Res..
[14] Neil D. Lawrence,et al. Deep Gaussian Processes , 2012, AISTATS.
[15] Catherine E. Powell,et al. An Introduction to Computational Stochastic PDEs , 2014 .
[16] Albert Cohen,et al. Approximation of high-dimensional parametric PDEs * , 2015, Acta Numerica.
[17] Wei Li,et al. Convolutional Neural Networks for Steady Flow Approximation , 2016, KDD.
[18] Hari Sundar,et al. FFT, FMM, or Multigrid? A comparative Study of State-Of-the-Art Poisson Solvers for Uniform and Nonuniform Grids in the Unit Cube , 2014, SIAM J. Sci. Comput..
[19] Roi Livni,et al. Learning Infinite-Layer Networks: Beyond the Kernel Trick , 2016, ArXiv.
[20] William H. Guss. Deep Function Machines: Generalized Neural Networks for Topological Layer Expression , 2016, ArXiv.
[21] E Weinan,et al. The Deep Ritz Method: A Deep Learning-Based Numerical Algorithm for Solving Variational Problems , 2017, Communications in Mathematics and Statistics.
[22] Jonas Adler,et al. Solving ill-posed inverse problems using iterative deep neural networks , 2017, ArXiv.
[23] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[24] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[25] Ronald A. DeVore,et al. Chapter 3: The Theoretical Foundation of Reduced Basis Methods , 2017 .
[26] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[27] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[28] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[29] Nicholas Zabaras,et al. Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification , 2018, J. Comput. Phys..
[30] Andrew M. Stuart,et al. How Deep Are Deep Gaussian Processes? , 2017, J. Mach. Learn. Res..
[31] Laurence Aitchison,et al. Deep Convolutional Networks as shallow Gaussian Processes , 2018, ICLR.
[32] Jan Eric Lenssen,et al. Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.
[33] Ryan L. Murphy,et al. Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs , 2018, ICLR.
[34] 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..
[35] Chi Chen,et al. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals , 2018, Chemistry of Materials.
[36] Leah Bar,et al. Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems , 2019, ArXiv.
[37] Karthik Duraisamy,et al. Prediction of aerodynamic flow fields using convolutional neural networks , 2019, Computational Mechanics.
[38] Leslie Pack Kaelbling,et al. Graph Element Networks: adaptive, structured computation and memory , 2019, ICML.
[39] Christoph Schwab,et al. Deep ReLU Neural Network Expression Rates for Data-to-QoI Maps in Bayesian PDE Inversion , 2020 .
[40] Nikola B. Kovachki,et al. Model Reduction and Neural Networks for Parametric PDEs , 2020, The SMAI journal of computational mathematics.