Uncertainty quantification for physics-informed deep learning
(2021) p.47-51- Abstract
- The development of physics-informed deep learning is radically changing compu-tational science and engineering, allowing for an effective integration ofphysics-based and datadriven modeling. Deep learning provides a powerful tool forthe
discovery of governing dynamics underneath data and enables nonlinear model-reduction. A Bayesian viewpoint of deep learning is discussed in this chapter towards the quantification of modeling uncertainties in physics-informed deep learning.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/282bf84a-a4ab-4fbd-83cc-3d43445f2d50
- author
- Guo, Mengwu LU and Brune, Christoph
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Mathematics: Key Enabling Technology for Scientific Machine Learning
- editor
- Schilders, W. H. A.
- pages
- 47 - 51
- publisher
- Dutch Platform for Mathematics
- language
- English
- LU publication?
- no
- id
- 282bf84a-a4ab-4fbd-83cc-3d43445f2d50
- alternative location
- https://platformwiskunde.nl/wp-content/uploads/2021/11/Math_KET_SciML.pdf
- date added to LUP
- 2024-03-23 22:15:09
- date last changed
- 2024-04-17 15:03:42
@misc{282bf84a-a4ab-4fbd-83cc-3d43445f2d50, abstract = {{The development of physics-informed deep learning is radically changing compu-tational science and engineering, allowing for an effective integration ofphysics-based and datadriven modeling. Deep learning provides a powerful tool forthe<br/> discovery of governing dynamics underneath data and enables nonlinear model-reduction. A Bayesian viewpoint of deep learning is discussed in this chapter towards the quantification of modeling uncertainties in physics-informed deep learning.}}, author = {{Guo, Mengwu and Brune, Christoph}}, booktitle = {{Mathematics: Key Enabling Technology for Scientific Machine Learning}}, editor = {{Schilders, W. H. A.}}, language = {{eng}}, pages = {{47--51}}, publisher = {{Dutch Platform for Mathematics}}, title = {{Uncertainty quantification for physics-informed deep learning}}, url = {{https://platformwiskunde.nl/wp-content/uploads/2021/11/Math_KET_SciML.pdf}}, year = {{2021}}, }