At the heart of many challenges in scientific research lie complex equations for which no analytical solutions exist. A new neural network model called DeepONet can learn to approximate nonlinear functions as well as operators.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Lu, L., Jin, P., Pang, G., Whang, W. & Karniadakis, G. Nat. Mach. Intell. https://doi.org/10.1038/s42256-021-00302-5 (2021).
Touvron, H., Vedaldi, A., Douze, M. & Jegou, H. In Advances in Neural Information Processing Systems Vol. 32 8252–8262 (NeurIPS, 2019).
Brown, T. et al. In Advances in Neural Information Processing Systems Vol. 33 1877–1901 (NeurIPS, 2020).
Mnih, V. et al. Nature 518, 529–533 (2015).
Silver, D. et al. Nature 529, 484–489 (2016).
Vinyals, O. et al. Nature 575, 350–354 (2019).
Cybenko, G. Math. Control Signals Syst. 2, 303–314 (1989).
Hornik, K., Stinchcombe, M. & White, H. Neural Netw. 2, 359–366 (1989).
Krizhevsky, A., Sutskever, I. & Hinton, G. In Advances in Neural Information Processing Systems Vol. 25 1097–1105 (NeurIPS, 2012).
Chen, T. & Chen, H. IEEE Trans. Neural Netw. 6, 911–917 (1995).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Higgins, I. Generalizing universal function approximators. Nat Mach Intell 3, 192–193 (2021). https://doi.org/10.1038/s42256-021-00318-x
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42256-021-00318-x
