Alireza Doostan

According to our database, Alireza Doostan authored at least 45 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

QuadConv: Quadrature-based convolutions with applications to non-uniform PDE data compression.

Kevin Doherty, Cooper Simpson, Stephen Becker, Alireza Doostan

J. Comput. Phys., February, 2024

2023

Simultaneous Identification and Denoising of Dynamical Systems.

Jeffrey M. Hokanson, Gianluca Iaccarino, Alireza Doostan

SIAM J. Sci. Comput., August, 2023

GenMod: A generative modeling approach for spectral representation of PDEs with random inputs.

Jacqueline Wentz, Alireza Doostan

J. Comput. Phys., 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

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

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

Bi-fidelity Variational Auto-encoder for Uncertainty Quantification.

Nuojin Cheng, Osman Asif Malik, Stephen Becker, Alireza Doostan

CoRR, 2023

2022

<i>S</i>-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

Neural network training using <i>ℓ</i>₁-regularization and bi-fidelity data.

Subhayan De, Alireza Doostan

J. Comput. Phys., 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

Software tools to enable immersive simulation.

Felix Newberry, Corey Wetterer-Nelson, John A. Evans, Alireza Doostan, Kenneth E. Jansen

Eng. Comput., 2022

QCNN: Quadrature Convolutional Neural Network with Application to Unstructured Data Compression.

Kevin Doherty, Cooper Simpson, Stephen Becker, Alireza Doostan

CoRR, 2022

Quadrature Sampling of Parametric Models with Bi-fidelity Boosting.

Nuojin Cheng, Osman Asif Malik, Yiming Xu, Stephen Becker, Alireza Doostan, Akil Narayan

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

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

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

A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study.

Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan

CoRR, 2022

2021

Neural Network Training Using 𝓁₁-Regularization and Bi-fidelity Data.

Subhayan De, 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

Deterministic matrix sketches for low-rank compression of high-dimensional simulation data.

Alec Michael Dunton, Alireza Doostan

CoRR, 2021

Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification.

Felix Newberry, Jerrad Hampton, Kenneth E. Jansen, Alireza Doostan

CoRR, 2021

A stochastic subspace approach to gradient-free optimization in high dimensions.

David Kozak, Stephen Becker, Alireza Doostan, Luis Tenorio

Comput. Optim. Appl., 2021

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

Pass-efficient methods for compression of high-dimensional turbulent flow data.

Alec M. Dunton, Lluís Jofre, Gianluca Iaccarino, Alireza Doostan

J. Comput. Phys., 2020

Sparse Identification of Nonlinear Dynamical Systems via Reweighted 𝓁s₁-regularized Least Squares.

Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan

CoRR, 2020

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

2019

Level set methods for stochastic discontinuity detection in nonlinear problems.

Per Pettersson, Alireza Doostan, Jan Nordström

J. Comput. Phys., 2019

Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach.

Subhayan De, Jerrad Hampton, Kurt Maute, Alireza Doostan

CoRR, 2019

2018

Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction.

Jerrad Hampton, Hillary R. Fairbanks, Akil Narayan, Alireza Doostan

J. Comput. Phys., 2018

Basis adaptive sample efficient polynomial chaos (BASE-PC).

Jerrad Hampton, Alireza Doostan

J. Comput. Phys., 2018

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

Optimization via separated representations and the canonical tensor decomposition.

Matthew J. Reynolds, Gregory Beylkin, Alireza Doostan

J. Comput. Phys., 2017

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

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 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

On polynomial chaos expansion via gradient-enhanced ℓ₁-minimization.

Ji Peng, Jerrad Hampton, Alireza Doostan

J. Comput. Phys., 2016

2015

Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies.

Jerrad Hampton, Alireza Doostan

J. Comput. Phys., 2015

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

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

A weighted l₁-minimization approach for sparse polynomial chaos expansions.

Ji Peng, Jerrad Hampton, Alireza Doostan

J. Comput. Phys., 2014

2011

A non-adapted sparse approximation of PDEs with stochastic inputs.

Alireza Doostan, Houman Owhadi

J. Comput. Phys., 2011

2010

A simplified model for seismic response prediction of concentrically braced frames.

Iman Hajirasouliha, Alireza Doostan

Adv. Eng. Softw., 2010

2009

A least-squares approximation of partial differential equations with high-dimensional random inputs.

Alireza Doostan, Gianluca Iaccarino

J. Comput. Phys., 2009

Padé-Legendre approximants for uncertainty analysis with discontinuous response surfaces.

Tonkid Chantrasmi, Alireza Doostan, Gianluca Iaccarino

J. Comput. Phys., 2009

2006

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