Multi-fidelity Gaussian process regression for prediction of random fields

By Lucia Parussini, Daniele Venturi, Paris Perdikaris, G. Em Karniadakis. 2017

116 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ellen Kuhl
2. F. Sahli Costabal
3. Daniel E. Hurtado
4. Joseph Homer Saleh
5. Zhaoyi Xu
6. Paris Perdikaris
7. G. Em Karniadakis
8. Maizar Raissi

In Computational Theory and Mathematics

1. Ellen Kuhl
2. F. Sahli Costabal
3. Daniel E. Hurtado
4. Joseph Homer Saleh
5. Zhaoyi Xu
6. Paris Perdikaris

In Statistics and Probability

1. Ellen Kuhl
2. F. Sahli Costabal
3. Daniel E. Hurtado
4. Joseph Homer Saleh
5. Zhaoyi Xu
6. Paris Perdikaris
7. G. Em Karniadakis
8. Sifan Wang

Citations in the corpus

• Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, methodology): “With a proper selection of Gaussian process kernel and corresponding hyperparameters, GPR and its variants such as active learning Kriging (ALK), can provide accurate response function estimation and uncertainty quantification [86], and they are increasingly used in engineering surrogate modeling an”
• Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019, methodology): “This approach has been widely used [26] and has been extended for efficiency [18] and to accommodate large data-sets [29].”