# ORTHOGONAL BASES FOR POLYNOMIAL REGRESSION WITH DERIVATIVE INFORMATION IN UNCERTAINTY QUANTIFICATION

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/orthogonal-bases-for-polynomial-regression-with-derivative-information-in/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yiou Li,Mihai Anitescu,Oleg Roderick,Fred J. Hickernell |
| Citations | 29 |
| DOI | 10.1615/int.j.uncertaintyquantification.2011002790 |
| Fields | Computer Science,Decision Sciences,Physics and Astronomy |
| Open Access | true |
| OA Status | bronze |
| OA URL | https://www.dl.begellhouse.com/download/article/2693fddd60c7231d/IJUQ104(p297-320).pdf |
| OpenAlex ID | https://openalex.org/W2111758728 |
| Type | article |
| Year | 2011 |

## Paper authors

- [Yiou Li](https://scholariq.org/researchers/yiou-li/)

## Paper journal

- [International Journal for Uncertainty Quantification](https://scholariq.org/journals/international-journal-for-uncertainty-quantification/)

## Paper primary topic

- [Probabilistic and Robust Engineering Design](https://scholariq.org/topics/probabilistic-and-robust-engineering-design/)

## Paper topics

- [Probabilistic and Robust Engineering Design](https://scholariq.org/topics/probabilistic-and-robust-engineering-design/)
- [Model Reduction and Neural Networks](https://scholariq.org/topics/model-reduction-and-neural-networks/)
- [Advanced Multi-Objective Optimization Algorithms](https://scholariq.org/topics/advanced-multi-objective-optimization-algorithms/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.