Peng Chen

CV

Contact

Peng Chen
The University of Texas at Austin -- ICES
201 E. 24th Street, Austin, Texas 78712
Email: peng@ices.utexas.edu
Phone: +1 512-232-3453
Office: POB 4.252

Research Interests


Professional Experience

Education


Publications

link to Google Scholar

Preprints

P. Chen and O. Ghattas
Sparse polynomial approximations for optimal control problems constrained by elliptic PDE with lognormal coefficient
preprint, 2017

P. Chen and O. Ghattas
Sparse polynomial approximations for affine parametric saddle point problems
preprint, 2017

P. Chen, O. Ghattas
Hessian-based sampling for goal-oriented model reduction with high-dimensional parameter
preprint, 2017

P. Chen, U. Villa, O. Ghattas
Taylor approximation and variance reduction for PDE-constrained optimal control problems under uncertainty
preprint, 2017

P. Chen
Sparse Quadrature for High-Dimensional Integration with Gaussian Measure
arXiv:1604.08466, 2016 (submitted)

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

Journal Publications

P. Chen, U. Villa, O. Ghattas
Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems
Computer Methods in Applied Mechanics and Engineering, in press, 2017
DOI: doi:10.1016/j.cma.2017.08.016

P. Chen, A. Quarteroni and G. Rozza
Reduced basis methods for uncertainty quantification
SIAM/ASA J. Uncertainty Quantification, 5(1):813-869, 2017
DOI: doi:10.1137/151004550

P. Chen and Ch. Schwab
Sparse grid, reduced basis Bayesian inversion: nonaffine-parametric nonlinear equations
Journal of Computational Physics, 316:470-503, 2016.
DOI: doi:10.1016/j.jcp.2016.02.055

P. Chen and Ch. Schwab
Sparse-grid, reduced-basis Bayesian inversion
Computer Methods in Applied Mechanics and Engineering, 279:84-115, 2015.
DOI: 10.1016/j.cma.2015.08.006

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.
DOI: 10.1016/j.jcp.2015.06.006

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.
DOI: 10.1007/s00211-015-0743-4

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.
DOI: 10.1007/s10915-013-9764-2

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.
DOI: 10.1051/m2an/2013128

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.
DOI:10.1137/130940517

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.
DOI:10.1016/j.cma.2013.08.016

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.
DOI: 10.1137/120884158

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.
DOI: 10.1137/130905253

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.
DOI: 10.1002/cnm.2554


Teaching

at ETH Zurich

at EPFL


Last update 04/05/2016 by Peng Chen