Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions

By Khader M. Hamdia, Mohammad Silani, Xiaoying Zhuang, Pengfei He, Timon Rabczuk. International Journal of Fracture, 2017

389 citations recorded by the source corpus.

Subfields: Polymers and Plastics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/651ec694bb15fb5e006d7cbbcb5364d399fe3ac0

How researchers see this paper

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In Statistics and Probability

1. Adam Scibior
2. Atilim Gunes Baydin
3. Frank D. Wood
4. Andreas Munk
5. Yura N. Perov
6. Jan-Willem van de Meent
7. Hongseok Yang
8. Zoubin Ghahramani

Citations in the corpus

• Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019, methodology): “Currently, the most commonly used methods for constructing deterministic surrogate models (Razavi et al., 2012) include Kriging (Simpson et al., 2001; Sacks et al., 1989), support vector machines (SVMs) (Willcox and Megretski, 2005), radial basis functions (RBFs) (Hussain et al., 2002; Regis and Sho”