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Preprints

K. Wu, T. O'Leary-Roseberry, P. Chen, and O. Ghattas.

Derivative-informed projected neural network for large-scale Bayesian optimal experimental design.

arXiv:2201.07925, 2022.

P. Chen, and J.O. Royset.

Performance bounds for PDE-constrained optimization under uncertainty.

arXiv:2102.06627, 2021.

K. Wu, P. Chen, and O. Ghattas.

A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement.

arXiv:2102.06627, 2021.

Y. Wang, P. Chen, and W. Li.

Projected Wasserstein gradient descent for high-dimensional Bayesian inference.

arXiv:2102.06350, 2021.

K. Wu, P. Chen, and O. Ghattas.

A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design.

arXiv:2010.15196, 2020.

Journal Publications

T. O'Leary-Roseberry, U. Villa, P. Chen, and O. Ghattas.

Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs.

Computer Methods in Applied Mechanics and Engineering, 388, 114199, 2022.

P. Chen and O. Ghattas.

Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters.

SIAM / ASA Journal on Uncertainty Quantification, 9(4), 1381-1410, 2021.

P. Chen and O. Ghattas.

Stein variational reduced basis Bayesian inversion.

SIAM Journal on Scientific Computing 43 (2), A1163-A1193, 2021.

P. Chen, K. Wu, and O. Ghattas.

Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities.

Computer Methods in Applied Mechanics and Engineering, 385, 114020, 2021.

P. Chen, M. Haberman, and O. Ghattas.

Optimal design of acoustic metamaterial cloaks under uncertainty.

Journal of Computational Physics, 431, 110114, 2021.

N. Alger, P. Chen, and O. Ghattas.

Tensor train construction from tensor actions, with application to compression of large high order derivative tensors.

SIAM Journal on Scientific Computing 42 (6), A3516-A3539, 2020.

P. Chen and O. Ghattas.

Hessian-based sampling for high-dimensional model reduction.

International Journal for Uncertainty Quantification, 9(2):103-121, 2019.

P. Chen, U. Villa, and O. Ghattas.

Taylor approximation and variance reduction for PDE-constrained optimal control problems under uncertainty.

Journal of Computational Physics, 385:163-186, 2019.

P. Chen.

Sparse Quadrature for High-Dimensional Integration with Gaussian Measure.

ESAIM: Mathematical Modelling and Numerical Analysis, 52(2):631-657, 2018.

P. Chen, U. Villa, and O. Ghattas.

Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems.

Computer Methods in Applied Mechanics and Engineering, 327:147-172 2017.

P. Chen, A. Quarteroni, and G. Rozza.

Reduced basis methods for uncertainty quantification.

SIAM/ASA J. Uncertainty Quantification, 5(1):813-869, 2017.

P. Chen and Ch. Schwab.

Sparse grid, reduced basis Bayesian inversion: nonaffine-parametric nonlinear equations.

Journal of Computational Physics, 316:470-503, 2016.

P. Chen and Ch. Schwab.

Sparse-grid, reduced-basis Bayesian inversion.

Computer Methods in Applied Mechanics and Engineering, 279:84-115, 2015.

P. Chen and A. Quarteroni.

A new algorithm for high-dimensional uncertainty quantification problems based on dimension-adaptive and reduced basis methods.

Journal of Computational Physics, 298:176-193, 2015.

P. Chen, A. Quarteroni, and G. Rozza.

Multilevel and weighted reduced basis method for stochastic optimal control problems constrained by Stokes equations.

Numerische Mathematik, 133(1):67-102, 2015.

P. Chen, A. Quarteroni, and G. Rozza.

Comparison between reduced basis and stochastic collocation methods for elliptic problems.

Journal of Scientific Computing, 59:187-216, 2014.

P. Chen, A. Quarteroni, and G. Rozza.

A weighted empirical interpolation method: A priori convergence analysis and applications.

ESAIM: Mathematical Modelling and Numerical Analysis, 48(04):943-953, 2014.

P. Chen and A. Quarteroni.

Weighted reduced basis method for stochastic optimal control problems with elliptic PDE constraint.

SIAM/ASA J. Uncertainty Quantification, 2(1):364-396, 2014.

P. Chen and A. Quarteroni.

Accurate and efficient evaluation of failure probability for partial differential equations with random input data.

Computer Methods in Applied Mechanics and Engineering, 267(0):233-260, 2013.

P. Chen, A. Quarteroni, and G. Rozza.

Stochastic optimal Robin boundary control problems of advection-dominated elliptic equations.

SIAM Journal on Numerical Analysis, 51(5):2700-2722, 2013.

P. Chen, A. Quarteroni, and G. Rozza.

A weighted reduced basis method for elliptic partial differential equation with random input data.

SIAM Journal on Numerical Analysis, 51(6):3163-3185, 2013.

P. Chen, A. Quarteroni, and G. Rozza.

Simulation-based Uncertainty quantification of human arterial network hemodynamics.

International Journal for Numerical Methods in Biomedical Engineering 29(6):698-721, 2013.

Proceedings

P. Chen and O. Ghattas.

Projected Stein variational gradient descent.

Advances in Neural Information Processing Systems (NeurIPS), 2020.

P. Chen, K. Wu, J. Chen, T. O'Leary-Roseberry, and O. Ghattas.

Projected Stein variational Newton: A fast and scalable Bayesian inference method in high dimensions.

Advances in Neural Information Processing Systems (NeurIPS), 2019.

N. Aretz-Nellesen, P. Chen, M.A. Grepl, and K. Veroy.

A sequential sensor selection strategy for hyper-parameterized linear Bayesian inverse problems.

Numerical Mathematics and Advanced Applications ENUMATH, 2019.

P. Chen, U. Villa, and O. Ghattas.

Taylor approximation for PDE-constrained optimization under uncertainty: Application to turbulent jet flow.

Proceedings in Applied Mathematics & Mechanics, 2018.

Book Chapters

P. Chen and Ch. Schwab.

Model order reduction methods in computational uncertainty quantification.

Handbook of Uncertainty Quantification. Editors R. Ghanem, D. Higdon and H. Owhadi. Springer, 2016.

P. Chen and Ch. Schwab.

Adaptive sparse grid model order reduction for fast Bayesian estimation and inversion.

Chapter in Sparse Grids and Applications - Stuttgart 2014, Editors: J. Garcke and D. Pflüger

Volume 109 of the series Lecture Notes in Computational Science and Engineering, Springer, 2016.

Theses

PhD Thesis

Model order reduction techniques for uncertainty quantification problems.

École polytechnique fédérale de Lausanne (EPFL), 2014.

Master Thesis

The Lattice Boltzmann Method for Fluid Dynamics: Theory and Applications.

École polytechnique fédérale de Lausanne (EPFL), 2011.