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SciML & UQ @ CSE Georgia Tech
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Preprints
J. Go, P. Chen.
Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator
arXiv:2409.09141, 2024.
P. Si, P. Chen.
Latent-EnSF: A latent ensemble score filter for high-dimensional data assimilation with sparse observation data
arXiv:2409.00127, 2024.
D. Luo, J. Chen, P. Chen, O. Ghattas.
Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs
arXiv:2408.06615, 2024.
B. Saleh, A. Zimmerman, P. Chen, O. Ghattas.
Tempered multifidelity importance sampling for gravitational wave parameter estimation
arXiv:2405.19407, 2024.
R. Orozco, F.J. Herrmann, P. Chen.
Probabilistic Bayesian optimal experimental design using conditional normalizing flows
arXiv:2402.18337, 2024.
J. Go, P. Chen.
Accelerating Bayesian optimal experimental design with derivative-informed neural operators
arXiv:2312.14810, 2023.
D. Luo, T. O'Leary-Roseberry, P. Chen, O. Ghattas.
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators.
arXiv:2305.20053, 2023.
A.M. Alghamdi, P. Chen, and M. Karamehmedovic.
Optimal design of photonic nanojets under uncertainty.
arXiv:2209.02454, 2022.
Journal Publications
Y. Wang, P. Chen, M. Pilanci, and W. Li.
Optimal neural network approximation of Wasserstein gradient direction via convex optimization.
to appear in SIAM Journal on Mathematics of Data Science, 2024.
D. Luo, P. Chen, T. O'Leary-Roseberry, U. Villa, O. Ghattas.
SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python
Journal of Open Source Software, 9(99), 6101, 2024.
T. O'Leary-Roseberry, P. Chen, U. Villa, O. Ghattas.
Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning
Journal of Computational Physics, 496, 112555, 2024.
N. Aretz, P. Chen, D. Degen, K. Veroy.
A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models
Journal of Computational Physics, 498, 112599, 2024.
L. Cao, K. Wu, J.T. Oden, P. Chen, O. Ghattas.
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate.
Computer Methods in Applied Mechanics and Engineering, 417, 116349, 2023.
D. Luo, L. Cao, P. Chen, O. Ghattas, and J.T. Oden.
Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm.
Journal of Computational Physics, 485, 112101, 2023.
P. Chen, and J.O. Royset.
Performance bounds for PDE-constrained optimization under uncertainty.
SIAM Journal on Optimization, 33 (3), 1828-1854, 2023.
K. Wu, T. O'Leary-Roseberry, P. Chen, and O. Ghattas.
Large-scale Bayesian optimal experimental design with derivative-informed projected neural network.
Journal of Scientific Computing, 95(1), 30, 2023.
K. Wu, P. Chen, and O. Ghattas.
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design.
SIAM / ASA Journal on Uncertainty Quantification, 11(1), 235-261, 2023.
K. Wu, P. Chen, and O. Ghattas.
An offline–online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement.
SIAM Journal on Scientific Computing, 45(1), B57-B77, 2023.
P. Chen and O. Ghattas.
Sparse polynomial approximations for affine parametric saddle point problems.
Vietnam Journal of Mathematics, 51, 151–175, 2023.
Y. Wang, P. Chen, and W. Li.
Projected Wasserstein gradient descent for high-dimensional Bayesian inference.
SIAM / ASA Journal on Uncertainty Quantification, 10(4), 1513-1532, 2022.
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
Y. Qiu, N. Bridges, P. Chen.
Derivative-enhanced deep operator network
Advances in Neural Information Processing Systems (NeurIPS), 2024.
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.