Peng ChenSciML & UQ Group

SciML & UQ Group

Publications

64 papers, newest first. Also on Google Scholar.

2026

  1. LEVDA: Latent ensemble variational data assimilation via differentiable dynamics

    P. Si, P. Chen

    Advances in Neural Information Processing Systems (NeurIPS), 2026

    ConferencearXiv

  2. Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs

    D. Luo, J. Chen, P. Chen, O. Ghattas

    SIAM/ASA Journal on Uncertainty Quantification, to appear, 2026

    JournalarXiv

  3. LD-EnFF: Latent-dynamics ensemble flow filtering for data assimilation with sparse observations

    Z. Tian, K. Shen, W. Hao, P. Si, P. Chen, W. Zhu

    arXiv:2610.04034, 2026

    PreprintarXiv

  4. C-STRIDE: An observation-driven AI digital twin for predicting basin-wide flood fields from sparse stream-gauge histories

    Y. Tong, P. Si, Y. Qiu, P. Chen

    arXiv:2609.39005, 2026

    PreprintarXiv

  5. Neural scaling laws of transformer operator network

    H. Yan, Z. Shi, Y. Xi, P. Chen, W. Liao

    arXiv:2609.33533, 2026

    PreprintarXiv

  6. Toward AI-driven digital twins for metropolitan floods: A conditional latent dynamics network surrogate of the shallow water equations

    P. Si, Y. Qiu, O. Sallam, J. Feinstein, Z. He, E. Yan, P. Chen

    Journal of Hydrology, 680, 136461, 2026

    JournalPaperarXivCode

  7. Dimension reduction for derivative-informed operator learning: An analysis of approximation errors

    D. Luo, T. O'Leary-Roseberry, P. Chen, O. Ghattas

    Journal of Machine Learning Research, 27(182), 1–94, 2026

    JournalPaperarXiv

  8. PolyMicros: A foundation model for polycrystalline material structure

    M.O. Buzzy, A.E. Robertson, P. Chen, S.R. Kalidindi

    Acta Materialia, 318, 122597, 2026

    JournalPaperarXiv

  9. Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation

    Y. Qiu, W. Dahmen, P. Chen

    Computer Methods in Applied Mechanics and Engineering, 461, 119228, 2026

    JournalPaperarXivCode

  10. LD-EnSF: Synergizing latent dynamics with ensemble score filters for fast data assimilation with sparse observations

    P. Xiao, P. Si, P. Chen

    International Conference on Learning Representations (ICLR), 2026

    ConferencePaperarXivCode

  11. Tucker tensor train Taylor series

    N. Alger, B. Christierson, P. Chen, O. Ghattas

    arXiv:2603.21141, 2026

    PreprintarXiv

  12. From sparse sensors to continuous fields: STRIDE for spatiotemporal reconstruction

    Y. Tong, P. Chen

    arXiv:2602.04201, 2026

    PreprintarXiv

  13. Sequential Bayesian optimal experimental design in infinite dimensions via policy gradient reinforcement learning

    K. Shen, P. Chen

    arXiv:2601.05868, 2026

    PreprintarXiv

2025

  1. PDPO: Parametric density path optimization

    S.G. Hernandez, P. Chen, H. Zhou

    Advances in Neural Information Processing Systems (NeurIPS), 2025

    ConferencearXivCode

  2. Efficient PDE-constrained optimization under high-dimensional uncertainty using derivative-informed neural operators

    D. Luo, T. O'Leary-Roseberry, P. Chen, O. Ghattas

    SIAM Journal on Scientific Computing, 47(4), C899–C931, 2025

    JournalPaperarXivCode

  3. Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator

    J. Go, P. Chen

    Journal of Computational Physics, 532, 113976, 2025

    JournalPaperarXiv

  4. Latent-EnSF: A latent ensemble score filter for high-dimensional data assimilation with sparse observation data

    P. Si, P. Chen

    International Conference on Learning Representations (ICLR), 2025

    ConferencePaperarXivCode

  5. Accurate, scalable, and efficient Bayesian optimal experimental design with derivative-informed neural operators

    J. Go, P. Chen

    Computer Methods in Applied Mechanics and Engineering, 438, 117845, 2025

    JournalPaperarXiv

2024

  1. Optimal neural network approximation of Wasserstein gradient direction via convex optimization

    Y. Wang, P. Chen, M. Pilanci, W. Li

    SIAM Journal on Mathematics of Data Science, 6(4), 978–999, 2024

    JournalPaperarXivCode

  2. Tempered multifidelity importance sampling for gravitational wave parameter estimation

    B. Saleh, A. Zimmerman, P. Chen, O. Ghattas

    Physical Review D, 110(10), 104037, 2024

    JournalPaperarXiv

  3. SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python

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

    Journal of Open Source Software, 9(99), 6101, 2024

    JournalPaperCode

  4. Derivative-informed neural operator: An efficient framework for high-dimensional parametric derivative learning

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

    Journal of Computational Physics, 496, 112555, 2024

    JournalPaperarXivCode

  5. A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems with correlated noise models

    N. Aretz, P. Chen, D. Degen, K. Veroy

    Journal of Computational Physics, 498, 112599, 2024

    JournalPaperarXiv

  6. Derivative-enhanced deep operator network

    Y. Qiu, N. Bridges, P. Chen

    Advances in Neural Information Processing Systems (NeurIPS), 2024

    ConferencearXivCode

  7. Probabilistic Bayesian optimal experimental design using conditional normalizing flows

    R. Orozco, F.J. Herrmann, P. Chen

    arXiv:2402.18337, 2024

    PreprintarXiv

2023

  1. Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate

    L. Cao, K. Wu, J.T. Oden, P. Chen, O. Ghattas

    Computer Methods in Applied Mechanics and Engineering, 417, 116349, 2023

    JournalPaperarXiv

  2. Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm

    D. Luo, L. Cao, P. Chen, O. Ghattas, J.T. Oden

    Journal of Computational Physics, 485, 112101, 2023

    JournalPaperarXiv

  3. Performance bounds for PDE-constrained optimization under uncertainty

    P. Chen, J.O. Royset

    SIAM Journal on Optimization, 33(3), 1828–1854, 2023

    JournalPaperarXiv

  4. Large-scale Bayesian optimal experimental design with derivative-informed projected neural network

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

    Journal of Scientific Computing, 95(1), 30, 2023

    JournalPaperarXiv

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

    K. Wu, P. Chen, O. Ghattas

    SIAM/ASA Journal on Uncertainty Quantification, 11(1), 235–261, 2023

    JournalPaperarXiv

  6. An offline–online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement

    K. Wu, P. Chen, O. Ghattas

    SIAM Journal on Scientific Computing, 45(1), B57–B77, 2023

    JournalPaperarXiv

  7. Sparse polynomial approximations for affine parametric saddle point problems

    P. Chen, O. Ghattas

    Vietnam Journal of Mathematics, 51, 151–175, 2023

    JournalPaperarXiv

2022

  1. Projected Wasserstein gradient descent for high-dimensional Bayesian inference

    Y. Wang, P. Chen, W. Li

    SIAM/ASA Journal on Uncertainty Quantification, 10(4), 1513–1532, 2022

    JournalPaperarXivCode

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

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

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

    JournalPaperarXiv

  3. Optimal design of photonic nanojets under uncertainty

    A.M. Alghamdi, P. Chen, M. Karamehmedovic

    arXiv:2209.02454, 2022

    PreprintarXiv

2021

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

    P. Chen, O. Ghattas

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

    JournalPaperarXiv

  2. Stein variational reduced basis Bayesian inversion

    P. Chen, O. Ghattas

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

    JournalPaperarXiv

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

    P. Chen, K. Wu, O. Ghattas

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

    JournalPaperarXivCode

  4. Optimal design of acoustic metamaterial cloaks under uncertainty

    P. Chen, M. Haberman, O. Ghattas

    Journal of Computational Physics, 431, 110114, 2021

    JournalPaperarXiv

2020

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

    N. Alger, P. Chen, O. Ghattas

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

    JournalPaper

  2. Projected Stein variational gradient descent

    P. Chen, O. Ghattas

    Advances in Neural Information Processing Systems (NeurIPS), 2020

    ConferencePDFarXivCode

2019

  1. Hessian-based sampling for high-dimensional model reduction

    P. Chen, O. Ghattas

    International Journal for Uncertainty Quantification, 9(2), 103–121, 2019

    JournalPaperarXiv

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

    P. Chen, U. Villa, O. Ghattas

    Journal of Computational Physics, 385, 163–186, 2019

    JournalPaper

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

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

    Advances in Neural Information Processing Systems (NeurIPS), 2019

    ConferencePaperarXivCode

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

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

    Numerical Mathematics and Advanced Applications ENUMATH 2019, 2019

    ConferencePaperarXiv

2018

  1. Sparse quadrature for high-dimensional integration with Gaussian measure

    P. Chen

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

    JournalPaperCode

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

    P. Chen, U. Villa, O. Ghattas

    Proceedings in Applied Mathematics & Mechanics, 2018

    ConferencePaper

2017

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

    P. Chen, U. Villa, O. Ghattas

    Computer Methods in Applied Mechanics and Engineering, 327, 147–172, 2017

    JournalPaperarXiv

  2. Reduced basis methods for uncertainty quantification

    P. Chen, A. Quarteroni, G. Rozza

    SIAM/ASA Journal on Uncertainty Quantification, 5(1), 813–869, 2017

    JournalPaper

2016

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

    P. Chen, Ch. Schwab

    Journal of Computational Physics, 316, 470–503, 2016

    JournalPaper

  2. Model order reduction methods in computational uncertainty quantification

    P. Chen, Ch. Schwab

    Handbook of Uncertainty Quantification, R. Ghanem, D. Higdon and H. Owhadi (eds.), Springer, 2016

    Book chapterPDF

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

    P. Chen, Ch. Schwab

    Sparse Grids and Applications – Stuttgart 2014, J. Garcke and D. Pflüger (eds.), Lecture Notes in Computational Science and Engineering 109, Springer, 2016

    Book chapterPaper

2015

  1. Sparse-grid, reduced-basis Bayesian inversion

    P. Chen, Ch. Schwab

    Computer Methods in Applied Mechanics and Engineering, 279, 84–115, 2015

    JournalPaper

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

    P. Chen, A. Quarteroni

    Journal of Computational Physics, 298, 176–193, 2015

    JournalPaper

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

    P. Chen, A. Quarteroni, G. Rozza

    Numerische Mathematik, 133(1), 67–102, 2015

    JournalPaper

2014

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

    P. Chen, A. Quarteroni, G. Rozza

    Journal of Scientific Computing, 59, 187–216, 2014

    JournalPaper

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

    P. Chen, A. Quarteroni, G. Rozza

    ESAIM: Mathematical Modelling and Numerical Analysis, 48(4), 943–953, 2014

    JournalPaper

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

    P. Chen, A. Quarteroni

    SIAM/ASA Journal on Uncertainty Quantification, 2(1), 364–396, 2014

    JournalPaper

  4. Model order reduction techniques for uncertainty quantification problems

    P. Chen

    PhD thesis, École polytechnique fédérale de Lausanne (EPFL), 2014

    ThesisPDF

2013

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

    P. Chen, A. Quarteroni

    Computer Methods in Applied Mechanics and Engineering, 267, 233–260, 2013

    JournalPaper

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

    P. Chen, A. Quarteroni, G. Rozza

    SIAM Journal on Numerical Analysis, 51(5), 2700–2722, 2013

    JournalPaper

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

    P. Chen, A. Quarteroni, G. Rozza

    SIAM Journal on Numerical Analysis, 51(6), 3163–3185, 2013

    JournalPaper

  4. Simulation-based uncertainty quantification of human arterial network hemodynamics

    P. Chen, A. Quarteroni, G. Rozza

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

    JournalPaper

2011

  1. The lattice Boltzmann method for fluid dynamics: Theory and applications

    P. Chen

    Master's thesis, École polytechnique fédérale de Lausanne (EPFL), 2011

    ThesisPaper