Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
DOI10.1016/j.cma.2019.112602zbMath1442.62008arXiv1905.03406OpenAlexW2970388146MaRDI QIDQ2179219
Francisco Sahli Costabal, Daniel E. Hurtado, Paris Perdikaris, Ellen Kuhl
Publication date: 12 May 2020
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1905.03406
Bayesian inferencemachine learningHamiltonian Monte Carlocardiac electrophysiologydata-driven modeling
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Related Items (8)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
- A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters
- The design and analysis of computer experiments.
- Multi-fidelity Gaussian process regression for prediction of random fields
- Machine learning in drug development: characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification
- A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling
- Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices
- Large Sample Properties of Simulations Using Latin Hypercube Sampling
- Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
- Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling
- Predicting the output from a complex computer code when fast approximations are available
- RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY
- Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets
- On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
This page was built for publication: Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models