Data-driven polynomial chaos expansion for machine learning regression

Data-driven polynomial chaos expansion for machine learning regression

9 August 2018

Papers citing "Data-driven polynomial chaos expansion for machine learning regression"

8 / 8 papers shown

Title
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 33 75 0 07 May 2023
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models Sibo Cheng Jianhua Chen Charitos Anastasiou P. Angeli Omar K. Matar Yi-Ke Guo Christopher C. Pain Rossella Arcucci AI4CE 44 60 0 07 Apr 2022
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty Paz Fink Shustin Shashanka Ubaru Vasileios Kalantzis L. Horesh H. Avron 29 2 0 10 Feb 2022
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing Patrick Link Miltiadis Poursanidis J. Schmid Rebekka Zache Martin von Kurnatowski U. Teicher S. Ihlenfeldt 14 16 0 04 Feb 2022
Global sensitivity analysis using derivative-based sparse Poincaré chaos expansions Nora Lüthen O. Roustant Fabrice Gamboa Bertrand Iooss S. Marelli Bruno Sudret 29 5 0 01 Jul 2021
Bayesian model inversion using stochastic spectral embedding Paul Wagner S. Marelli Bruno Sudret 17 14 0 15 May 2020
Replication-based emulation of the response distribution of stochastic simulators using generalized lambda distributions Xujia Zhu Bruno Sudret 17 30 0 20 Nov 2019
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach C. Lataniotis S. Marelli Bruno Sudret 23 66 0 15 Dec 2018