Fetching the paper…
Reading the bibliography…
Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains.
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
Tianping Chen and Hong Chen · 1995
Earlier work this paper cites.
Matplotlib: A 2d graphics environment
John D Hunter · 2007
Earlier work this paper cites.
Compositional pattern producing networks: A novel abstraction of development
Kenneth O Stanley · 2007
Earlier work this paper cites.
Real analysis: measure theory, integration, and Hilbert spaces
Elias M Stein and Rami Shakarchi · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Spectral networks and deep locally connected networks on graphs. arxiv
J Bruna, W Zaremba, A Szlam, and Y LeCun · 2014
Earlier work this paper cites.
Geometric measure theory
Herbert Federer · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Earlier work this paper cites.
Modulating early visual processing by language
Harm De Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C Courville · 2017
Earlier work this paper cites.
Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Earlier work this paper cites.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Earlier work this paper cites.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Aladin Virmaux and Kevin Scaman · 2018
Earlier work this paper cites.
Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Earlier work this paper cites.
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Earlier work this paper cites.
On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
Earlier work this paper cites.
Disn: Deep implicit surface network for high-quality single-view 3d reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann · 2019
Earlier work this paper cites.
Deep local shapes: Learning local sdf priors for detailed 3d reconstruction
Rohan Chabra, Jan E Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
Earlier work this paper cites.
Implicit functions in feature space for 3d shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
Array programming with numpy
Charles R Harris, K Jarrod Millman, Stéfan J Van Der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, et al · 2020
Earlier work this paper cites.
Lipschitz constant estimation of neural networks via sparse polynomial optimization
Fabian Latorre, Paul Rolland, and Volkan Cevher · 2020
Earlier work this paper cites.
Neural operator: Graph kernel network for partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2020
Earlier work this paper cites.
Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
Cited alongside, same era.
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
Cited alongside, same era.
Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
Cited alongside, same era.
Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
Cited alongside, same era.
Patchnets: Patch-based generalizable deep implicit 3d shape representations
Edgar Tretschk, Ayush Tewari, Vladislav Golyanik, Michael Zollhöfer, Carsten Stoll, and Christian Theobalt · 2020
Cited alongside, same era.
On layer normalization in the transformer architecture
The cost-accuracy trade-off in operator learning with neural networks
Maarten V de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, and Andrew M Stuart · 2022
Later among the works it cites.
Generic bounds on the approximation error for physics-informed (and) operator learning
Tim De Ryck and Siddhartha Mishra · 2022
Later among the works it cites.
Towards multi-spatiotemporal-scale generalized pde modeling
Jayesh K Gupta and Johannes Brandstetter · 2022
Later among the works it cites.
Error estimates for deeponets: A deep learning framework in infinite dimensions
Samuel Lanthaler, Siddhartha Mishra, and George E Karniadakis · 2022
Later among the works it cites.
Transformer for partial differential equations’ operator learning
Zijie Li, Kazem Meidani, and Amir Barati Farimani · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tieyan Liu · 2020
Cited alongside, same era.
Model reduction and neural networks for parametric PDEs
Kaushik Bhattacharya, Bamdad Hosseini, Nikola B Kovachki, and Andrew M Stuart · 2021
Cited alongside, same era.
Choose a transformer: Fourier or galerkin
Shuhao Cao · 2021
Cited alongside, same era.
pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein · 2021
Cited alongside, same era.
Unconstrained scene generation with locally conditioned radiance fields
Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W Taylor, and Joshua M Susskind · 2021
Cited alongside, same era.
Multiwavelet-based operator learning for differential equations
Gaurav Gupta, Xiongye Xiao, and Paul Bogdan · 2021
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
Cited alongside, same era.
Later among the works it cites.
Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
Later among the works it cites.
Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
Later among the works it cites.
Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis EH Tay, Wei Liu, Yonghong Tian, and Li Yuan · 2022
Later among the works it cites.
Attention beats concatenation for conditioning neural fields
Daniel Rebain, Mark J Matthews, Kwang Moo Yi, Gopal Sharma, Dmitry Lagun, and Andrea Tagliasacchi · 2022
Later among the works it cites.
NOMAD: Nonlinear manifold decoders for operator learning
Jacob H Seidman, Georgios Kissas, Paris Perdikaris, and George J. Pappas · 2022
Later among the works it cites.
Pdebench: An extensive benchmark for scientific machine learning
Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, and Mathias Niepert · 2022
Later among the works it cites.
Wavelet neural operator: a neural operator for parametric partial differential equations
Tapas Tripura and Souvik Chakraborty · 2022
Later among the works it cites.
Improved architectures and training algorithms for deep operator networks
Sifan Wang, Hanwen Wang, and Paris Perdikaris · 2022
Later among the works it cites.
Neural fields in visual computing and beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar · 2022
Later among the works it cites.
Lno: Laplace neural operator for solving differential equations
Qianying Cao, Somdatta Goswami, and George Em Karniadakis · 2023
Later among the works it cites.
Spectral neural operators
VS Fanaskov and Ivan V Oseledets · 2023
Later among the works it cites.
Gnot: A general neural operator transformer for operator learning
Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, and Jun Zhu · 2023
Later among the works it cites.
Neural operator: Learning maps between function spaces with applications to pdes
Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2023
Later among the works it cites.
Multiple physics pretraining for physical surrogate models
Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Geraud Krawezik, Francois Lanusse, et al · 2023
Later among the works it cites.
Variational autoencoding neural operators
Jacob H Seidman, Georgios Kissas, George J. Pappas, and Paris Perdikaris · 2023
Later among the works it cites.
Operator learning with neural fields: Tackling pdes on general geometries
Louis Serrano, Lise Le Boudec, Armand Kassaï Koupaï, Thomas X Wang, Yuan Yin, Jean-Noël Vittaut, and Patrick Gallinari · 2023
Later among the works it cites.
An expert’s guide to training physics-informed neural networks
Sifan Wang, Shyam Sankaran, Hanwen Wang, and Paris Perdikaris · 2023
Later among the works it cites.
Universal physics transformers
Benedikt Alkin, Andreas Fürst, Simon Schmid, Lukas Gruber, Markus Holzleitner, and Johannes Brandstetter · 2024
Closest in time.
Neural operators for accelerating scientific simulations and design
Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, and Anima Anandkumar · 2024
Closest in time.
Dpot: Auto-regressive denoising operator transformer for large-scale pde pre-training
Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, and Jun Zhu · 2024
Closest in time.
Neural operators with localized integral and differential kernels
Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, and Anima Anandkumar · 2024
Closest in time.