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07/03/2023
A non-intrusive bi-fidelity reduced basis method for time-independent problems
Scientific and engineering problems often involve parametric partial dif...
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03/13/2023
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion
Bayesian Physics Informed Neural Networks (B-PINNs) have gained signific...
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06/25/2022
Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread
When investigating epidemic dynamics through differential models, the pa...
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02/20/2022
Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations
Multiscale elliptic equations with scale separation are often approximat...
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12/02/2021
Multi-fidelity methods for uncertainty propagation in kinetic equations
The construction of efficient methods for uncertainty quantification in ...
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10/27/2021
Bi-fidelity stochastic collocation methods for epidemic transport models with uncertainties
Uncertainty in data is certainly one of the main problems in epidemiolog...
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07/20/2021
A bi-fidelity stochastic collocation method for transport equations with diffusive scaling and multi-dimensional random inputs
In this paper, we consider the development of efficient numerical method...
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04/07/2020
Nonnegativity-Enforced Gaussian Process Regression
Gaussian Process (GP) regression is a flexible non-parametric approach t...
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02/01/2019
Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling
In this paper, we present a new nonintrusive reduced basis method when a...
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12/07/2018
