Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond
By Paris Perdikaris, G. Em Karniadakis. 2016
102 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, 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 Artificial Intelligence
- G. Em Karniadakis
- Yibo Yang
- Paris Perdikaris
- Guofei Pang
- Ellen Kuhl
- Maizar Raissi
- Phaedon-Stelios Koutsourelakis
- Constantin Grigo
In Computational Theory and Mathematics
- Yibo Yang
- Paris Perdikaris
- G. Em Karniadakis
- Ellen Kuhl
- F. Sahli Costabal
- Guofei Pang
- K. Matsuno
- Jiang Yao
In Statistics and Probability
- Paris Perdikaris
- G. Em Karniadakis
- Yibo Yang
- Ellen Kuhl
- F. Sahli Costabal
- Guofei Pang
- Phaedon-Stelios Koutsourelakis
- Constantin Grigo
Citations in the corpus
- Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (2019, methodology): “As discussed in section 1, these methods have been successful in a wide spectrum of applications including design optimization [1, 2, 3, 4, 5], model calibration [7, 8, 9], and uncertainty quantification [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21].”
- Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification (2019, methodology): “In a wide variety of engineering problems, Gaussian process regression has been applied to multi-fidelity problems [32, 49].”
- Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization (2017, methodology): “The method has been successfully applied to the calibration of outflow boundary conditions in blood flow simulations, as well as the parameter estimation in elliptic PDEs [13].”
- Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media (2017, background): “One of the strengths of such tools stems from the use of lower-fidelity models that retain some of the underlying physics and as such produce outputs that are strongly correlated/dependent with the high-fidelity ones [61].”
- Bayesian Optimization for Adaptive Experimental Design: A Review (2020, background): “Multi-fidelity Bayesian optimisation has been demonstrated in [113], [114].”