On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data. - Roger G. Ghanem; Alireza Doostan (J. Comput. Phys., 2006)

Padé-Legendre approximants for uncertainty analysis with discontinuous response surfaces. - Tonkid Chantrasmi; Alireza Doostan; Gianluca Iaccarino (J. Comput. Phys., 2009)

A least-squares approximation of partial differential equations with high-dimensional random inputs. - Alireza Doostan; Gianluca Iaccarino (J. Comput. Phys., 2009)

A simplified model for seismic response prediction of concentrically braced frames. - Iman Hajirasouliha; Alireza Doostan (Adv. Eng. Softw., 2010)

A non-adapted sparse approximation of PDEs with stochastic inputs. - Alireza Doostan; Houman Owhadi (J. Comput. Phys., 2011)

A weighted l1-minimization approach for sparse polynomial chaos expansions. - Ji Peng; Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2014)

Variational Multiscale Analysis: The Fine-Scale Green's Function for Stochastic Partial Differential Equations. - Jayanth Jagalur-Mohan; Onkar Sahni; Alireza Doostan; Assad A. Oberai (SIAM/ASA J. Uncertain. Quantification, 2014)

Smoothed aggregation algebraic multigrid for stochastic PDE problems with layered materials. - Marian Brezina; Alireza Doostan; Tom Manteuffel; Steve F. McCormick; John W. Ruge (Numer. Linear Algebra Appl., 2014)

Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies. - Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2015)

On polynomial chaos expansion via gradient-enhanced ℓ1-minimization. - Ji Peng; Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2016)

A well-posed and stable stochastic Galerkin formulation of the incompressible Navier-Stokes equations with random data. - Per Pettersson; Jan Nordström; Alireza Doostan (J. Comput. Phys., 2016)

Randomized Alternating Least Squares for Canonical Tensor Decompositions: Application to A PDE With Random Data. - Matthew J. Reynolds; Alireza Doostan; Gregory Beylkin (SIAM J. Sci. Comput., 2016)

A low-rank control variate for multilevel Monte Carlo simulation of high-dimensional uncertain systems. - Hillary R. Fairbanks; Alireza Doostan; Christian Ketelsen; Gianluca Iaccarino (J. Comput. Phys., 2017)

Optimization via separated representations and the canonical tensor decomposition. - Matthew J. Reynolds; Gregory Beylkin; Alireza Doostan (J. Comput. Phys., 2017)

Time-dependent global sensitivity analysis with active subspaces for a lithium ion battery model. - Paul G. Constantine; Alireza Doostan (Stat. Anal. Data Min., 2017)

Basis adaptive sample efficient polynomial chaos (BASE-PC). - Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2018)

Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction. - Jerrad Hampton; Hillary R. Fairbanks; Akil Narayan 001; Alireza Doostan (J. Comput. Phys., 2018)

Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach. - Subhayan De; Jerrad Hampton; Kurt Maute; Alireza Doostan (CoRR, 2019)

Level set methods for stochastic discontinuity detection in nonlinear problems. - Per Pettersson; Alireza Doostan; Jan Nordström (J. Comput. Phys., 2019)

On transfer learning of neural networks using bi-fidelity data for uncertainty propagation. - Subhayan De; Jolene Britton; Matthew J. Reynolds; Ryan Skinner; Kenneth E. Jansen; Alireza Doostan (CoRR, 2020)

Sparse Identification of Nonlinear Dynamical Systems via Reweighted 𝓁s1-regularized Least Squares. - Alexandre Cortiella; Kwang-Chun Park; Alireza Doostan (CoRR, 2020)

Pass-efficient methods for compression of high-dimensional turbulent flow data. - Alec M. Dunton; Lluís Jofre; Gianluca Iaccarino; Alireza Doostan (J. Comput. Phys., 2020)

Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence. - Hillary R. Fairbanks; Lluís Jofre; Gianluca Geraci; Gianluca Iaccarino; Alireza Doostan (J. Comput. Phys., 2020)

A stochastic subspace approach to gradient-free optimization in high dimensions. - David Kozak; Stephen Becker; Alireza Doostan; Luis Tenorio (Comput. Optim. Appl., 2021)

Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification. - Felix Newberry; Jerrad Hampton; Kenneth E. Jansen; Alireza Doostan (CoRR, 2021)

Deterministic matrix sketches for low-rank compression of high-dimensional simulation data. - Alec Michael Dunton; Alireza Doostan (CoRR, 2021)

Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning. - Subhayan De; Bhuiyan Shameem Mahmood Ebna Hai; Alireza Doostan; Markus Bause (CoRR, 2021)

Neural Network Training Using 𝓁1-Regularization and Bi-fidelity Data. - Subhayan De; Alireza Doostan (CoRR, 2021)

A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study. - Alexandre Cortiella; Kwang-Chun Park; Alireza Doostan (CoRR, 2022)

Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles. - Aaron Allred; Lauren J. Abbott; Alireza Doostan; Kurt Maute (CoRR, 2022)

Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets. - Subhayan De; Malik Hassanaly; Matthew J. Reynolds; Ryan N. King; Alireza Doostan (CoRR, 2022)

Fast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems. - Osman Asif Malik; Yiming Xu; Nuojin Cheng; Stephen Becker; Alireza Doostan; Akil Narayan 001 (CoRR, 2022)

Quadrature Sampling of Parametric Models with Bi-fidelity Boosting. - Nuojin Cheng; Osman Asif Malik; Yiming Xu; Stephen Becker; Alireza Doostan; Akil Narayan 001 (CoRR, 2022)

QCNN: Quadrature Convolutional Neural Network with Application to Unstructured Data Compression. - Kevin Doherty; Cooper Simpson; Stephen Becker; Alireza Doostan (CoRR, 2022)

Software tools to enable immersive simulation. - Felix Newberry; Corey Wetterer-Nelson; John A. Evans; Alireza Doostan; Kenneth E. Jansen (Eng. Comput., 2022)

Task-parallel in situ temporal compression of large-scale computational fluid dynamics data. - Heather Pacella; Alec M. Dunton; Alireza Doostan; Gianluca Iaccarino (Int. J. High Perform. Comput. Appl., 2022)

Neural network training using ℓ1-regularization and bi-fidelity data. - Subhayan De; Alireza Doostan (J. Comput. Phys., 2022)

S-frame discrepancy correction models for data-informed Reynolds stress closure. - Eric L. Peters; Riccardo Balin; Kenneth E. Jansen; Alireza Doostan; John A. Evans (J. Comput. Phys., 2022)

Bi-fidelity Variational Auto-encoder for Uncertainty Quantification. - Nuojin Cheng; Osman Asif Malik; Stephen Becker; Alireza Doostan (CoRR, 2023)

In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD. - Riccardo Balin; Filippo Simini; Cooper Simpson; Andrew Shao; Alessandro Rigazzi; Matthew Ellis; Stephen Becker; Alireza Doostan; John A. Evans; Kenneth E. Jansen (CoRR, 2023)

PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model. - Malik Hassanaly; Peter J. Weddle; Ryan N. King; Subhayan De; Alireza Doostan; Corey R. Randall; Eric J. Dufek; Andrew M. Colclasure; Kandler Smith (CoRR, 2023)

PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model. - Malik Hassanaly; Peter J. Weddle; Ryan N. King; Subhayan De; Alireza Doostan; Corey R. Randall; Eric J. Dufek; Andrew M. Colclasure; Kandler Smith (CoRR, 2023)

GenMod: A generative modeling approach for spectral representation of PDEs with random inputs. - Jacqueline Wentz; Alireza Doostan (J. Comput. Phys., 2023)

Simultaneous Identification and Denoising of Dynamical Systems. - Jeffrey M. Hokanson; Gianluca Iaccarino; Alireza Doostan (SIAM J. Sci. Comput., 2023)

Online randomized interpolative decomposition with a posteriori error estimator for temporal PDE data reduction. - Angran Li; Stephen Becker; Alireza Doostan (CoRR, 2024)

Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data. - Grant Norman; Jacqueline Wentz; Hemanth Kolla; Kurt Maute; Alireza Doostan (CoRR, 2024)

Ensemble WSINDy for Data Driven Discovery of Governing Equations from Laser-based Full-field Measurements. - Abigail C. Schmid; Alireza Doostan; Fatemeh Pourahmadian (CoRR, 2024)

Subsampling of Parametric Models with Bifidelity Boosting. - Nuojin Cheng; Osman Asif Malik; Yiming Xu; Stephen Becker; Alireza Doostan; Akil Narayan 001 (SIAM/ASA J. Uncertain. Quantification, 2024)

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression. - Kevin Doherty; Cooper Simpson; Stephen Becker; Alireza Doostan (J. Comput. Phys., 2024)

Physically Interpretable Representation and Controlled Generation for Turbulence Data. - Tiffany Fan; Murray Cutforth; Marta D'Elia; Alexandre Cortiella; Alireza Doostan; Eric Darve (CoRR, 2025)

Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search. - Nuojin Cheng; Alireza Doostan; Stephen Becker (CoRR, 2025)

Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method. - Conor Rowan; Kurt Maute; Alireza Doostan (CoRR, 2025)

On the definition and importance of interpretability in scientific machine learning. - Conor Rowan; Alireza Doostan (CoRR, 2025)

Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient. - Conor Rowan; John A. Evans; Kurt Maute; Alireza Doostan (CoRR, 2025)

Risk-Aware Aerocapture Guidance Through a Probabilistic Indicator Function. - Grace E. Calkins; Jay W. McMahon; Alireza Doostan; David C. Woffinden (CoRR, 2025)