Peng Chen

Contact

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

Research Interests


Professional Experience

Education


Publications

link to Google Scholar

Preprints

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 goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement
arXiv:2102.06627, 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

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

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
accepted in Computer Methods in Applied Mechanics and Engineering, 2021

P. Chen and O. Ghattas
Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
accepted in SIAM / ASA Journal on Uncertainty Quantification, 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
DOI: doi.org/10.1051/m2an/2018012

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
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

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


Teaching

at ETH Zurich

at EPFL


Last update 04/05/2016 by Peng Chen