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Journal Papers

  • D. Schiavazzi, A. Doostan, G. Iaccarino, and A. L. Marsden, A Generalized Multi-resolution Expansion for Uncertainty Propagation, submitted, May 2016.
  • M. Reynolds, G. Beylkin, and A. Doostan, Optimization via Separated Representations and Canonical Tensor Decompositions, submitted, April 2016.
  • M. Balducci, B. Jones, and A. Doostan, Orbit Uncertainty Propagation With Separated Representations, submitted, December 2015.
  1. M. Reynolds, A. Doostan, and G. Beylkin, Randomized Alternating Least Squares for Canonical Tensor Decompositions:
    Application to A PDE With Random Data, SIAM Journal on Scientific Computing, accepted, May 2016. (link)
  2. J. Peng, J. Hampton, and A. Doostan, On Polynomial Chaos Expansion via Gradient-enhanced l1-minimization, Journal of Computational Physics, 310, 2016, 440-458. (link)
  3. P. Pettersson, J. Nordstrom, and A. Doostan, A Well-posed and Stable Stochastic Galerkin Formulation of the Incompressible Navier-Stokes Equations with Random Data, Journal of Computational Physics, 306, 2016, 92-116. (link)
  4. J. Feldhacker, B. Jones, A. Doostan, and J. Hampton, Reduced Cost Maneuver Design Using Surrogate Models, Advances in Space Research, 57, 2016, 588-603. (link)
  5. M. Hadigol, K. Maute, and A. Doostan, On Uncertainty Quantification of Lithium-ion Batteries: Application to an LiC_6/LiCoO_2 cell, Journal of Power Sources, 300, 2015, 507-524. (link)
  6. C. Lang, A. Sharma, A. Doostan, and K. Maute, Heaviside Enriched Extended Stochastic FEM for Problems with Uncertain Material Interfaces, Computational Mechanics, 56, 2015, 753-767. (link)
  7. U. Akalp, S. Chu, S. Skaalure, S. Bryant, A. Doostan, and F. Vernerey, Determination of the Polymer-Solvent Interaction Parameter for PEG Hydrogels in Water: Application of a Self Learning Algorithm, Polymer, 66, 2015, 135-147. (link)
  8. J. Hampton and A. Doostan, Coherence motivated sampling and convergence analysis of least-squares polynomial chaos regression, Computer Methods in Applied Mechanics and Engineering, 290, 2015, 73-97. (link)
  9. B. Jones, N. Parish, and A. Doostan, Post-maneuver collision probability estimation using sparse polynomial chaos expansions, AIAA Journal of Guidance, Control, and Dynamics, 8, 2015, 1425-1437. (link)
  10. J. Hampton and A. Doostan, Compressive sampling of chaos expansions: convergence analysis and sampling strategies, Journal of Computational Physics, 280, 2015, 363-386. (link)
  11. C. Lang, D. Makhija, A. Doostan, and K. Maute, A simple and efficient preconditioning scheme for Heaviside enriched XFEM, Computational Mechanics, 54, 2014, 1357-1374. (link)
  12. J. Jagalur-Mohan, O. Sahni, A. Doostan, and A. Oberai, Variational multiscale analysis: the fine-scale Green's function for stochastic partial differential equations, SIAM/ASA Journal on Uncertainty Quantification, 2, 2014, 397-422. (link)
  13. D. Schiavazzi, A. Doostan, and G. Iaccarino, Sparse multiresolution regression for uncertainty propagation, International Journal for Uncertainty Quantification, 4, 2014, 303-331. (link)
  14. J. Peng, J. Hampton, and A. Doostan, A weighted l1-minimization approach for sparse polynomial chaos expansions, Journal of Computational Physics, 267, 2014, 92-111. (link)
  15. M. Hadigol, A. Doostan, H. Matthies, and R. Niekamp. Partitioned treatment of uncertainty in coupled domain problems: A separated representation approach, Computer Methods in Applied Mechanics and Engineering, 274, 2014,103-124. (link)
  16. M. Brezina, A. Doostan, T. Manteuffel, S. McCormick, and J. Ruge, Smoothed aggregation algebraic multigrid for SPDE problems with layered materials, Numerical Linear Algebra with Applications, 21, 2014, 239-255.
  17. B. Jones and A. Doostan, Satellite collision probability estimation using polynomial chaos, Advances in Space Research, 52(11), 2013, 1860-1875.
  18. A. Doostan, A. Validi, and G. Iaccarino, A non-intrusive low-rank separated representations of high-dimensional stochastic models, Computer Methods in Applied Mechanics and Engineering, 263, 2013, 42-55.
  19. C. Lang, A. Doostan, and K. Maute, Extended stochastic FEM for heat transfer analysis with uncertain material interfaces, Computational Mechanics, 51(6), 2013, 1031-1049.
  20. B. Jones, A. Doostan, and G. Born, Nonlinear propagation of orbit uncertainty using non-intrusive polynomial chaos, AIAA Journal of Guidance, Control, and Dynamics, 36(2), 2013, 430-444.
  21. P. Pettersson, A. Doostan, and J. Nordstrom, On stability and monotonicity requirements of discretized stochastic conservation laws with random viscosity, Computer Methods in Applied Mechanics and Engineering, 258, 2013, 134-151.
  22. L. Mehrez, A. Doostan, D. Moens, and D. Vandepitte, Stochastic identification of composite material properties from limited experimental databases, Part II: Uncertainty modeling, Mechanical Systems and Signal Processing, 27, 2012, 484-498.
  23. A. Doostan and H. Owhadi, A non-adapted sparse approximation of PDEs with stochastic inputs, Journal of Computational Physics, 230(8), 2011, 3015-3034. (As of December 2015 listed among JCP's most cited articles published from 2010-2015)
  24. I.H. Rasouliha and A. Doostan, A simplified model for seismic response prediction of concentrically braced frames, Advances in Engineering Software, 41(3), 2010, 497-505.
  25. A. Doostan and G. Iaccarino, A least-squares approximation of partial differential equations with high-dimensional random inputs, Journal of Compational Physics, 228 (12), 2009, 4332-4345
  26. P. Constantine, A. Doostan, and G. Iaccarino, A hybrid collocation/Galerkin scheme for convective heat transfer problems with stochastic boundary conditions, International Journal for Numerical Methods in Engineering, 2009, DOI: 10.1002/nme.2564
  27. T. Chanstrasmi, A. Doostan, and G. Iaccarino, Pade-Legendre approximants for uncertainty analysis with discontinuous response surfaces, Journal of Computational Physics, 228 (19), 2009, 7159-7180
  28. R. Ghanem, A. Doostan, and J. Red-Horse, A probabilistic construction of model validation, Computer Methods in Applied Mechanics and Engineering, 197 (29-32), 2008, 2585-2595
  29. R. Ghanem, G. Saad, and A. Doostan, Efficient solution of stochastic systems: Application to the embankment dam problem, Structural Safety, 29 (3), 2007, 238-251
  30. A. Doostan, R. Ghanem, and J. Red-Horse, Stochastic model reduction for chaos representations, Computer Methods in Applied Mechanics and Engineering, 196 (37-40), 2007, 3951-3966
  31. R. Ghanem and A. Doostan, On the construction and analysis of stochastic models: Characterization and propagation of the errors associated with limited data, Journal of Computational Physics, 217, 2006, 63-81
  32. H. Moghaddam, I.H. Rasouliha, and A. Doostan, Optimum seismic design of concentrically braced steel frames: concepts and design procedures, Journal of Constructional Steel Research, 61, 2005, 151-166

Book Chapters


  1. J. Hampton and A. Doostan, Compressive Sampling Methods for Sparse Polynomial Chaos Expansions, Handbook of Uncertainty Quantification, accepted October 2015.

Conference Proceedings

  1. M. Hadigol, A. Doostan, H. Matthies, and R. Niekamp. Partitioned Solution of Coupled Stochastic Problems, ECCOMAS Book Series: Coupled Problems, Springer, 2014.
  2. D. Schiavazzi, A. Doostan, and G. Iaccarino, Sparse multiresolution regression for uncertainty propagation, International Workshop on Uncertainty Quantification in Fluids Simulation (BOQUSE 2013), Bordeaux, France, December 16-18, 2013.
  3. D. Schiavazzi, A. Doostan, and G. Iaccarino. A sparse multiresolution stochastic approximation for uncertainty quantification, Contemporary Mathematics, 586, 2013, 295-303.
  4. M. Balducci, B. Jones, and A. Doostan, Orbit uncertainty propagation with separated representations, 2013 AAS/AIAA Astrodynamics Specialist Conference, Hilton Head, SC, August 11-15, 2013.
  5. B. Jones, N. Parish, M. Werner, and A. Doostan, Post-maneuver collision probability estimation using polynomial chaos, 2013 AAS/AIAA Astrodynamics Specialist Conference, Hilton Head, SC, August 11-15, 2013.
  6. B. Jones, A. Doostan, and G. Born, Conjunction assessment using polynomial chaos expansions, 23rd International Symposium on Space Flight Dynamics, ISSFD23, Pasadena, CA, October 29 - November 2, 2012.
  7. P. Constantine, Q. Wang, A. Doostan, and G. Iaccarino, A surrogate accelerated Bayesian inverse analysis of the HyShot II flight data 13th AIAA Non-Deterministic Approaches Conference, Denver, CO, April 4-7, 2011
  8. L. Mehrez, A. Doostan, D. Moens, and D. Vandepitte, A Validation Study of a Stochastic Representation of Composite Material Properties from Limited Experimental Data, USD2010 International Conference on Uncertainty in Structural Dynamics, Leuven, Belgium, September 20-22, 2010
  9. G. Iaccarino, R. Pecnik, V.E. Terrapon, and A. Doostan, Numerical predictions of the performance in flight of an air-breathing hypersonic vehicle: HyShot II, Proceedings of the ASME IMECE2010, Vancouver, Canada, 2010
  10. P. Constantine, A. Doostan, and G. Iaccarino, A hybrid uncertainty propagation scheme for convective heat transfer problems, 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Schaumburg, IL, April 7-10, 2008
  11. R. Ghanem, J. Red-Horse, and A. Doostan, Modeling and propagation of uncertainty with application to prediction validation, Symposium on Computational Uncertainty, Applied Vehicle Technology Panel (AVT), Athens, Greece, October 1-4, 2007
  12. R. Ghanem, J. Red-Horse, A. Benjamin, and A. Doostan, Stochastic process model for material properties under incomplete information, 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Honolulu, Hawaii, April 23- 26, 2007
  13. A. Doostan and R. Ghanem, Characterization of stochastic system parameters from experimental data: A Bayesian inference approach, 9th International Conference on Structural Safety and Reliability, Rome, Italy, June 19-23, 2005
  14. R. Ghanem and A. Doostan, A-posteriori error estimates for the spectral stochastic FEM based on hierarchical bases, ASCE 9th Joint Specialty Conference on Probabilistic Mechanics & Structural Reliability, PMC2004, Albuquerque, NM, July 26-28, 2004
  15. H. Moghaddam, I.H. Rasouliha, and A. Doostan, Proper distribution of seismic loads for design of steel frames, 6th International conference on civil engineering, Isfahan, Iran, May 2003
  16. I.H. Rasouliha, H. Moghaddam, and A. Doostan, On the optimum strength distribution in seismic design of structures, Response of structures to extreme loading, XL2003, Toronto, Canada, August 3-6 2003
  17. I.H. Rasouliha, H. Moghaddam, and A. Doostan, On the optimum seismic design of structures, International Conference in Earthquake Engineering, Skopje-Ohrid, Macedonia, August 26-29, 2003

Technical Reports

  1. P. Constantine, A. Doostan, Q. Wang, and G. Iaccarino, A surrogate accelerated Bayesian inverse analysis of the HyShot II flight data, Center for turbulence research, Summer School Proceedings, Stanford University, 2010
  2. J. Witteveen, A. Doostan, R. Pecnik, and G. Iaccarino, Uncertainty quantification of the transonic flow around the RAE2822 airfoil, Center for turbulence research, Annual Research Briefs 2009, Stanford University, 2009
  3. A. Doostan, H. Owhadi, A. Lashgari, and G. Iaccarino, Non-adapted sparse approxi- mation of PDEs with stochastic inputs, Center for turbulence research, Annual Research Briefs 2009, Stanford University, 2009
  4. A. Doostan, G. Iaccarino, Breaking the curse of dimensionality for a class of PDEs with stochastic inputs, Center for turbulence research, Annual Research Briefs 2008, Stanford University, 2008
  5. A.Doostan, G.Iaccarino, and N.Etemadi,Aleast-squaresapproximationofhigh-dimensional uncertain systems, Center for turbulence research, Annual Research Briefs 2007, Stanford University, 2007