Optimal Model Management for Multifidelity Monte Carlo Estimation
By Benjamin Peherstorfer, Karen E. Willcox, Max D. Gunzburger. 2016
257 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, 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 Computational Theory and Mathematics
- Yibo Yang
- Paris Perdikaris
- Max D. Gunzburger
- Benjamin Peherstorfer
- Xianglin Liu
- Zongrui Pei
- Jiaxin Zhang
- Karen E. Willcox
In Statistical and Nonlinear Physics
- Paris Perdikaris
- Yibo Yang
- Benjamin Peherstorfer
- Max D. Gunzburger
- Karen E. Willcox
- Xianglin Liu
- Zongrui Pei
- Jiaxin Zhang
In Statistics and Probability
- Paris Perdikaris
- Yibo Yang
- Benjamin Peherstorfer
- Karen E. Willcox
- Max D. Gunzburger
- Nicholas Zabaras
- Xianglin Liu
- Zongrui Pei
Citations in the corpus
- Adversarial Uncertainty Quantification in Physics-Informed Neural Networks (2018, background): “However, despite the fact that much progress has been made over the last two decades, the most popular methods for scientific computing under uncertainty, such as polynomial chaos expansions [17, 18, 19], sparse grid quadratures [20, 21], multi-level/multi-fidelity Monte Carlo sampling [22, 23], pro”
- Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, methodology): “For instance, for a 250-atom CuZn supercell, the first-principles Monte-Carlo simulation requires the self-consistent (SCF) calculations of 600,000 DFT energies [46], which can easily consume a million CPU core-hours.”
- Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “We refer to [164] for an in-depth discussion of the interaction between the models and for a more detailed analysis.”
- Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media (2018, methodology): “Many efforts have been undertaken to improve the computational efficiency of MC methods, for example, multifidelity MC (O'Malley et al., 2018; Peherstorfer et al., 2016) and multilevel MC (Lu et al.”
- Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020, background): “As such, this field finds extensive application in control [8, 9, 10, 11], multi-fidelity optimization [12], uncertainty quantification [13, 14] and data-assimilation [15] among others.”