The Random Feature Model for Input-Output Maps between Banach Spaces

20 May 2020

Papers citing "The Random Feature Model for Input-Output Maps between Banach Spaces"

36 / 36 papers shown

Title
PODNO: Proper Orthogonal Decomposition Neural Operators Zilan Cheng Zhongjian Wang Li-Lian Wang Mejdi Azaiez 34 0 0 25 Apr 2025
On the choice of the non-trainable internal weights in random feature maps Pinak Mandal Georg Gottwald Nicholas Cranch TPM 40 1 0 07 Aug 2024
Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability Fields Daniel Badawi Eduardo Gildin OOD AI4CE 13 4 0 13 Jul 2024
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features Oliver R. A. Dunbar Nicholas H. Nelsen Maya Mutic 25 5 0 30 Jun 2024
Discretization Error of Fourier Neural Operators S. Lanthaler Andrew M. Stuart Margaret Trautner 35 4 0 03 May 2024
Inverse Problems with Learned Forward Operators Simon Arridge Andreas Hauptmann Yury Korolev 28 1 0 21 Nov 2023
Error Bounds for Learning with Vector-Valued Random Features S. Lanthaler Nicholas H. Nelsen 27 12 0 26 May 2023
Nonlocality and Nonlinearity Implies Universality in Operator Learning S. Lanthaler Zong-Yi Li Andrew M. Stuart 18 16 0 26 Apr 2023
Score-based Diffusion Models in Function Space Jae Hyun Lim Nikola B. Kovachki Ricardo Baptista Christopher Beckham Kamyar Azizzadenesheli ... Karsten Kreis Jan Kautz Christopher Pal Arash Vahdat Anima Anandkumar DiffM 70 37 0 14 Feb 2023
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Arnulf Jentzen Adrian Riekert Philippe von Wurstemberger 21 1 0 07 Feb 2023
An Enhanced V-cycle MgNet Model for Operator Learning in Numerical Partial Differential Equations Jianqing Zhu Juncai He Qiumei Huang 30 4 0 02 Feb 2023
Learning Functional Transduction Mathieu Chalvidal Thomas Serre Rufin VanRullen AI4CE 32 2 0 01 Feb 2023
KoopmanLab: machine learning for solving complex physics equations Wei Xiong Muyuan Ma Xiaomeng Huang Ziyang Zhang Pei Sun Yang Tian AI4CE 26 13 0 03 Jan 2023
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics Esha Saha L. Ho Giang Tran 36 5 0 11 Nov 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 24 17 0 27 Oct 2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani Nicholas Kramer Runa Eschenhagen Lorenzo Rosasco Philipp Hennig UQCV BDL 13 9 0 02 Aug 2022
Asymptotic Consistency for Nonconvex Risk-Averse Stochastic Optimization with Infinite Dimensional Decision Spaces Johannes Milz T. Surowiec 23 4 0 29 Jul 2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning Thomas O'Leary-Roseberry Peng Chen Umberto Villa Omar Ghattas AI4CE 29 39 0 21 Jun 2022
NOMAD: Nonlinear Manifold Decoders for Operator Learning Jacob H. Seidman Georgios Kissas P. Perdikaris George J. Pappas AI4CE 23 68 0 07 Jun 2022
Transformer for Partial Differential Equations' Operator Learning Zijie Li Kazem Meidani A. Farimani 42 140 0 26 May 2022
Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs Peiyan Hu Qi Meng Bin Chen Shiqi Gong Yue Wang Wei Chen Rongchan Zhu Zhirui Ma Tie-Yan Liu AI4CE 19 11 0 13 Apr 2022
MIONet: Learning multiple-input operators via tensor product Pengzhan Jin Shuai Meng Lu Lu 20 155 0 12 Feb 2022
Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes Junhyung Park Krikamol Muandet 14 6 0 09 Feb 2022
Learning Operators with Coupled Attention Georgios Kissas Jacob H. Seidman Leonardo Ferreira Guilhoto V. Preciado George J. Pappas P. Perdikaris 24 109 0 04 Jan 2022
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces Hao Liu Haizhao Yang Minshuo Chen T. Zhao Wenjing Liao 32 36 0 01 Jan 2022
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks Thomas O'Leary-Roseberry Xiaosong Du A. Chaudhuri J. Martins Karen E. Willcox Omar Ghattas 30 22 0 14 Dec 2021
Physics-Informed Neural Operator for Learning Partial Differential Equations Zong-Yi Li Hongkai Zheng Nikola B. Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli Anima Anandkumar AI4CE 62 380 0 06 Nov 2021
Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces George Stepaniants 46 15 0 26 Aug 2021
Learning the optimal Tikhonov regularizer for inverse problems Giovanni S. Alberti E. De Vito Matti Lassas Luca Ratti Matteo Santacesaria 20 30 0 11 Jun 2021
Choose a Transformer: Fourier or Galerkin Shuhao Cao 39 220 0 31 May 2021
Two-layer neural networks with values in a Banach space Yury Korolev 21 23 0 05 May 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sifan Wang Hanwen Wang P. Perdikaris AI4CE 38 661 0 19 Mar 2021
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM Oliver R. A. Dunbar A. Garbuno-Iñigo T. Schneider Andrew M. Stuart 17 58 0 24 Dec 2020
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 34 29 0 11 Dec 2020
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 205 2,282 0 18 Oct 2020
Multipole Graph Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 15 371 0 16 Jun 2020