Neural field (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Neural field" in English language version.

Last modified: 02:43:51, 2 March 2026 (UTC)

Ref.Un. Ref.Website

Global rank English rank

265doi.org

2^nd place

223worldcat.org

4^th place

201wiley.com

150^th place

174^th place

1915arxiv.org

49^th place

31^st place

11sciencedirect.com

137^th place

146^th place

11adsabs.harvard.edu

14^th place

9^th place

11osti.gov

2,098^th place

1,146^th place

adsabs.harvard.edu (Global: 14^th place; English: 9^th place)

ui.adsabs.harvard.edu

Raissi, M.; Perdikaris, P.; Karniadakis, G. E. (2019-02-01). "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations". Journal of Computational Physics. 378: 686–707. Bibcode:2019JCoPh.378..686R. doi:10.1016/j.jcp.2018.10.045. ISSN 0021-9991. OSTI 1595805.

arxiv.org (Global: 49^th place; English: 31^st place)

Quarteroni, Alfio; Gervasio, Paola; Regazzoni, Francesco (2025). "Combining physics-based and data-driven models: Advancing the frontiers of research with scientific machine learning". Mathematical Models and Methods in Applied Sciences. 35 (4): 905–1071. arXiv:2501.18708. doi:10.1142/S0218202525500125.
Rahaman, Nasim; Baratin, Aristide; Arpit, Devansh; Draxler, Felix; Lin, Min; Hamprecht, Fred A.; Bengio, Yoshua; Courville, Aaron (2018). "On the Spectral Bias of Neural Networks". arXiv:1806.08734 [stat.ML].
Sitzmann, Vincent; Martel, Julien N. P.; Bergman, Alexander W.; Lindell, David B.; Wetzstein, Gordon (2020). "Implicit Neural Representations with Periodic Activation Functions". arXiv:2006.09661 [cs.CV].
Tancik, Matthew; Srinivasan, Pratul P.; Mildenhall, Ben; Fridovich-Keil, Sara; Raghavan, Nithin; Singhal, Utkarsh; Ramamoorthi, Ravi; Barron, Jonathan T.; Ng, Ren (2020). "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains". arXiv:2006.10739 [cs.CV].
Qi, Charles R.; Su, Hao; Mo, Kaichun; Guibas, Leonidas J. (2016). "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation". arXiv:1612.00593 [cs.CV].
Jeong Joon Park; Florence, Peter; Straub, Julian; Newcombe, Richard; Lovegrove, Steven (2019). "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation". arXiv:1901.05103 [cs.CV].
Serrano, Louis; Lise Le Boudec; Armand Kassaï Koupaï; Thomas X Wang; Yin, Yuan; Vittaut, Jean-Noël; Gallinari, Patrick (2023). "Operator Learning with Neural Fields: Tackling PDEs on General Geometries". arXiv:2306.07266 [cs.LG].
Ha, David; Dai, Andrew; Le, Quoc V. (2016). "HyperNetworks". arXiv:1609.09106 [cs.LG].
Perez, Ethan; Strub, Florian; Harm de Vries; Dumoulin, Vincent; Courville, Aaron (2017). "FiLM: Visual Reasoning with a General Conditioning Layer". arXiv:1709.07871 [cs.CV].
Finn, Chelsea; Abbeel, Pieter; Levine, Sergey (2017). "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks". arXiv:1703.03400 [cs.LG].
Sitzmann, Vincent; Chan, Eric R.; Tucker, Richard; Snavely, Noah; Wetzstein, Gordon (2020). "MetaSDF: Meta-learning Signed Distance Functions". arXiv:2006.09662 [cs.CV].
Luisa M Zintgraf; Shiarlis, Kyriacos; Kurin, Vitaly; Hofmann, Katja; Whiteson, Shimon (2018). "Fast Context Adaptation via Meta-Learning". arXiv:1810.03642 [cs.LG].
Mescheder, Lars; Oechsle, Michael; Niemeyer, Michael; Nowozin, Sebastian; Geiger, Andreas (2018). "Occupancy Networks: Learning 3D Reconstruction in Function Space". arXiv:1812.03828 [cs.CV].
Mildenhall, Ben; Srinivasan, Pratul P.; Tancik, Matthew; Barron, Jonathan T.; Ramamoorthi, Ravi; Ng, Ren (2020). "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis". arXiv:2003.08934 [cs.CV].
Yin, Yuan; Kirchmeyer, Matthieu; Franceschi, Jean-Yves; Rakotomamonjy, Alain; Gallinari, Patrick (2022). "Continuous PDE Dynamics Forecasting with Implicit Neural Representations". arXiv:2209.14855 [cs.LG].

doi.org (Global: 2^nd place; English: 2^nd place)

doi.org

Xie, Yiheng; Takikawa, Towaki; Saito, Shunsuke; Litany, Or; Yan, Shiqin; Khan, Numair; Tombari, Federico; Tompkin, James; Sitzmann, Vincent; Sridhar, Srinath (2022). "Neural Fields in Visual Computing and Beyond". Computer Graphics Forum. 41 (2): 641–676. doi:10.1111/cgf.14505. ISSN 1467-8659.
Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert (1989-01-01). "Multilayer feedforward networks are universal approximators". Neural Networks. 2 (5): 359–366. doi:10.1016/0893-6080(89)90020-8. ISSN 0893-6080.
Quarteroni, Alfio; Gervasio, Paola; Regazzoni, Francesco (2025). "Combining physics-based and data-driven models: Advancing the frontiers of research with scientific machine learning". Mathematical Models and Methods in Applied Sciences. 35 (4): 905–1071. arXiv:2501.18708. doi:10.1142/S0218202525500125.
Raissi, M.; Perdikaris, P.; Karniadakis, G. E. (2019-02-01). "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations". Journal of Computational Physics. 378: 686–707. Bibcode:2019JCoPh.378..686R. doi:10.1016/j.jcp.2018.10.045. ISSN 0021-9991. OSTI 1595805.

dx.doi.org

Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert (1989-01-01). "Multilayer feedforward networks are universal approximators". Neural Networks. 2 (5): 359–366. doi:10.1016/0893-6080(89)90020-8. ISSN 0893-6080.

osti.gov (Global: 2,098^th place; English: 1,146^th place)

Raissi, M.; Perdikaris, P.; Karniadakis, G. E. (2019-02-01). "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations". Journal of Computational Physics. 378: 686–707. Bibcode:2019JCoPh.378..686R. doi:10.1016/j.jcp.2018.10.045. ISSN 0021-9991. OSTI 1595805.

sciencedirect.com (Global: 137^th place; English: 146^th place)

Raissi, M.; Perdikaris, P.; Karniadakis, G. E. (2019-02-01). "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations". Journal of Computational Physics. 378: 686–707. Bibcode:2019JCoPh.378..686R. doi:10.1016/j.jcp.2018.10.045. ISSN 0021-9991. OSTI 1595805.

wiley.com (Global: 150^th place; English: 174^th place)

onlinelibrary.wiley.com

Xie, Yiheng; Takikawa, Towaki; Saito, Shunsuke; Litany, Or; Yan, Shiqin; Khan, Numair; Tombari, Federico; Tompkin, James; Sitzmann, Vincent; Sridhar, Srinath (2022). "Neural Fields in Visual Computing and Beyond". Computer Graphics Forum. 41 (2): 641–676. doi:10.1111/cgf.14505. ISSN 1467-8659.

worldcat.org (Global: 4^th place; English: 4^th place)

search.worldcat.org

Xie, Yiheng; Takikawa, Towaki; Saito, Shunsuke; Litany, Or; Yan, Shiqin; Khan, Numair; Tombari, Federico; Tompkin, James; Sitzmann, Vincent; Sridhar, Srinath (2022). "Neural Fields in Visual Computing and Beyond". Computer Graphics Forum. 41 (2): 641–676. doi:10.1111/cgf.14505. ISSN 1467-8659.
Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert (1989-01-01). "Multilayer feedforward networks are universal approximators". Neural Networks. 2 (5): 359–366. doi:10.1016/0893-6080(89)90020-8. ISSN 0893-6080.
Raissi, M.; Perdikaris, P.; Karniadakis, G. E. (2019-02-01). "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations". Journal of Computational Physics. 378: 686–707. Bibcode:2019JCoPh.378..686R. doi:10.1016/j.jcp.2018.10.045. ISSN 0021-9991. OSTI 1595805.

Neural field (English Wikipedia)

adsabs.harvard.edu (Global: 14th place; English: 9th place)

ui.adsabs.harvard.edu

arxiv.org (Global: 49th place; English: 31st place)

doi.org (Global: 2nd place; English: 2nd place)