Publications

MURI Special Session: AIAA Non-Deterministic Approaches/Multidisciplinary Optimization Conference (SciTech) 2020

  1. Danial Khatamsaz, Lalith Peddareddygari, Sam Friedman and Douglas Allaire: Efficient Multi-Information Source Multiobjective Bayesian Optimization, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  2. Xiaosong Du, Ping He and Joaquim Martins: A B-Spline-based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  3. Anirban Chaudhuri, Benjamin Peherstorfer and Karen Willcox: Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  4. Anirban Chaudhuri, Matthew Norton and Boris Kramer: Risk-Based Design Optimization Via Probability of Failure, Conditional Value-at-Risk, and Buffered Probability of Failure, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  5. Pengchao Song, X.Q. Wang and Marc Mignolet: Multi-Fidelity Reduced Order Modeling of Representative Hypersonic Panel, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.


MURI Special Session: AIAA Non-Deterministic Approaches Conference (SciTech) 2018

  1. R. Lam, M. Poloczek, P.I. Frazier and K. Willcox: Advances in Bayesian Optimization with Applications in Aerospace Engineering, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  2. P. Song, and M.P. Mignolet: Maximum Entropy-based Uncertainty Modeling at the Finite Element Level, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  3. A. Chaudhuri, J. Jasa, J.R.R.A. Martins and K. Willcox: Multifidelity Optimization Under Uncertainty for a Tailless Aircraft, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  4. B. Peherstorfer, P. Beran and K. Willcox: Multifidelity Monte Carlo Estimation for Large-Scale Uncertainty Propagation, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  5. S. Friedman, B. Isaac and D. Allaire: Efficient Decoupling of Multiphysics Systems for Uncertainty Propagation, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  6. B. D. Tracey and D. Wolpert: Upgrading from Gaussian Processes to Student’s-T Processes, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.


RT1: Optimal information-source management

  1. R. Lam, O. Zahm, Y. Marzouk, and K. Willcox: Multifidelity Dimension Reduction via Active Subspaces, SIAM Journal on Scientific Computing, Vol. 42, No. 2, pp. A929-A956, 2020.
  2. A. Chaudhuri, B. Kramer, and K. Willcox: Information Reuse for Importance Sampling in Reliability-Based Design Optimization, Reliability Engineering and System Safety, Vol. 201, pp. 106853, 2020.
  3. J. Wang, S.C. Clark, E. Liu, and P.I. Frazier: Parallel Bayesian Global Optimization of Expensive Functions, Operations Research, https://doi.org/10.1287/opre.2019.1966, 2020.
  4. R. Astudillo, and P.I. Frazier: Multi-Attribute Bayesian Optimization With Interactive Preference Learning, Artificial Intelligence and Statistics (AISTATS), 2020.
  5. S. Cakmak, R. Astudillo, P.I. Frazier, and E. Zhou, Bayesian Optimization of Risk Measures, 2020. (under review)
  6. R. Astudillo, and P.I. Frazier, Bayesian optimization of Function Networks, 2020. (under review)
  7. S. Toscano-Palmerin, and P.I. Frazier, Bayesian optimization with expensive integrands, 2020. (under review)
  8. R. Couperthwaite, A. Molkeri, D. Khatamsaz, A. Srivastava, D. Allaire, and R. Arroyave: Materials Design through Batch Bayesian Optimization with Multi-Source Information Fusion, JOM, 2020. (under review)
  9. D, Khatamsaz, L. Peddareddygari, S. Friedman, D. Allaire: Bayesian Optimization of Multi-Objective Functions Using Multiple Information Sources, AIAA Journal, 2020. (under review)
  10. D. Khatamsaz, A. Molkeri, R. Couperthwaite, J. James, R. Arroyave, D. Allaire, A. Srivastave: Efficiently Exploiting Process-Structure-Property Relationships in Material Design by Multi-Information Source Fusion, Acta Materialia, 2020. (under review)
  11. A. Chaudhuri, A. Marques, and K. Willcox: mfEGRA: Multifidelity Efficient Global Reliability Analysis. Oden Institute Report 19-16, 2020. (under review)
  12. A. Chaudhuri, B. Peherstorfer and K. Willcox: Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  13. A. Chaudhuri, M. Norton and B. Kramer: Risk-Based Design Optimization Via Probability of Failure, Conditional Value-at-Risk, and Buffered Probability of Failure, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  14. J. Wu, and P.I. Frazier: Practical Two-Step Lookahead Bayesian Optimization, Neural Information Processing Systems (NeurIPS), 2019.
  15. R. Astudillo, and P.I. Frazier: Bayesian Optimization of Composite Functions, International Conference on Machine Learning (ICML), 2019.
  16. P. Yang, K. Iyer, and P.I. Frazier: Information Design in Spatial Resource Competition, The 15th Conference on Web and Internet Economics (WINE), 2019.
  17. J. Wu, S. Toscano, A.G. Wilson, and P.I. Frazier: Practical Multi-fidelity Bayesian Optimization of Iterative Machine Learning Algorithms, Conference on Uncertainty in Artificial Intelligence (UAI), 2019.
  18. B. Kramer, A. Marques, B. Peherstorfer, U. Villa, and K. Willcox: Multifidelity probability estimation via fusion of estimators, Journal of Computational Physics, Vol. 392, pp. 385-402, 2019.
  19. S.F. Ghoreishi, S. Friedman, and D. Allaire: Adaptive Dimensionality Reduction for Fast Sequential Optimization with Gaussian Processes, ASME Journal of Mechanical Design, Vol. 141, No. 7, pp. 071404, 2019.
  20. S.F. Ghoreishi, W.D. Thomison, and D. Allaire: Sequential Information-Theoretic and Reification-Based Approach for Querying Multi-Information Sources, AIAA Journal of Aerospace Information Systems, Vol. 16, No. 12, pp. 575-587, 2019.
  21. B. Isaac, and D. Allaire: Expensive Black-Box Model Optimization via a Gold Rush Policy, Journal of Mechanical Design, Vol. 141, No. 3, pp. 031401-031401-9, 2019.
  22. A. Chaudhuri, A. Marques, R. Lam, and K. Willcox: Reusing information for multifidelity active learning in reliability-based design optimization, 21st AIAA Non-Deterministic Approaches Conference (AIAA Scitech), San Diego, CA, January 2019.
  23. P. I. Frazier, S. G. Henderson, and R. Waeber: Probabilistic Bisection Converges Almost as Quickly as Stochastic Approximation. Mathematics of Operations Research, Vol. 44, No. 2, pp. 651-667, 2019.
  24. A. Kolchinsky, B.D. Tracey, and D. Wolpert: Nonlinear Information Bottleneck, Entropy, Vol. 21, No. 12, pp. 1181, 2019.
  25. M. A. Bouhlel, and J. R. R. A. Martins: Gradient-enhanced kriging for high-dimensional problems, Engineering with Computers, Vol. 35, No. 1, pp. 157-173, 2019.
  26. B. Peherstorfer, K. Willcox, and M. Gunzburger: Survey of multifidelity methods in uncertainty propagation, inference, and optimization, SIAM Review, Vol. 60, No. 3, pp. 550-591, 2018.
  27. A. Marques, R. Lam, and K. Willcox: Contour location via entropy reduction leveraging multiple information sources, Advances In Neural Information Processing Systems (NeurIPS) 31, pp. 5223-5233, 2018.
  28. B. Peherstorfer, M. Gunzburger, and K. Willcox: Convergence analysis of multifidelity Monte Carlo estimation, Numerische Mathematik, Vol. 139, No. 3, pp. 683-707, 2018, https://doi.org/10.1007/s00211-018-0945-7.
  29. S.F. Ghoreishi, and D. Allaire: Multi-information source constrained Bayesian optimization, Structural and Multidisciplinary Optimization, Vol. 59, No. 3, pp. 977-991, 2019.
  30. A.M. Saxe, Y. Bansal, J. Dapello, M. Advani, A. Kolchinsky, B.D. Tracey, and D.D. Cox: On the information bottleneck theory of deep learning, International Conference on Learning Representations, 2018.
  31. A. Chaudhuri, J. Jasa, J.R.R.A. Martins and K. Willcox: Multifidelity Optimization Under Uncertainty for a Tailless Aircraft, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  32. R. Lam, M. Poloczek, P.I. Frazier and K. Willcox: Advances in Bayesian Optimization with Applications in Aerospace Engineering, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  33. B. Peherstorfer, P. Beran and K. Willcox: Multifidelity Monte Carlo Estimation for Large-Scale Uncertainty Propagation, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  34. B. D. Tracey and D. Wolpert: Upgrading from Gaussian Processes to Student’s-T Processes, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  35. S. F. Ghoreishi and D. Allaire: A Fusion-Based Multi-Information Source Optimization Approach using Knowledge Gradient Policies, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech), Kissimmee, FL, January 2018.
  36. D. Freund, M. Poloczek, and D. Reichman: Contagious Sets in Dense Graphs, European Journal of Combinatorics, 68, pp. 66-78, 2018.
  37. R. Lam and K. Willcox: Lookahead Bayesian Optimization with Inequality Constraints. In Advances in Neural Information Processing Systems, pages 1888-1898, 2017.
  38. B. Chen, P.I. Frazier: Dueling Bandits with Weak Regret, International Conference on Machine Learning (ICML), 2017.
  39. R. Astudillo Marban and P.I. Frazier: Multi-Attribute Bayesian Optimization under Utility Uncertainty, NIPS Workshop on Bayesian Optimization (BayesOpt 2017), 2017.
  40. J. Wu and P.I. Frazier: Continuous-Fidelity Bayesian Optimization with Knowledge Gradient, NIPS Workshop on Bayesian Optimization (BayesOpt 2017), 2017.
  41. W. Han, P. Rajan, P.I. Frazier, and B.M. Jedynak: Probabilistic Group Testing under Sum Observations: A Parallelizable 2-Approximation for Entropy Loss, IEEE Transactions on Information Theory, vol 63, issue 2, pp 915--933, 2017.
  42. M. Poloczek and D. P. Williamson: An experimental evaluation of fast approximation algorithms for the maximum satisfiability problem, ACM Journal of Experimental Algorithmics (JEA) 22 (1), 1.6, 2017.
  43. N. Dong, D. Eckman, M. Poloczek, X. Zhao, and S. Henderson: Comparing the Finite-Time Performance of Simulation-Optimization Algorithms, In Proc. of Winter Simulation Conference (WSC), pp. 2206-2217, 2017.
  44. S. Toscano-Palmerin, and P.I. Frazier: Stratified Bayesian Optimization, Proceedings of the 12th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC), 2017.
  45. J. Wu, M. Poloczek, A. Wilson, and P.I. Frazier: Bayesian Optimization with Gradients, Neural Information Processing Systems (NIPS), 2017.
  46. M. Poloczek, J. Wang, and P.I. Frazier: Multi-Information Source Optimization, Neural Information Processing Systems (NIPS), 2017.
  47. A. Kolchinsky, and B.D. Tracey: Estimating Mixture Entropy with Pairwise Distances, Entropy, Vol. 19, No. 7, pp. 361, 2017, https://dx.doi.org/10.3390/e19070361.
  48. B.D. Tracey, and D. Wolpert: Reducing the Error of Monte Carlo Algorithms by Learning Control Variates. (submitted)
  49. R. Lam, K. Willcox, and D. Wolpert: Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach, In Advances In Neural Information Processing Systems (NIPS) 29, pp. 883-891, 2016.
  50. A. Chaudhuri, D. Wolpert, and B. Tracey: Stochastic optimization and machine learning: cross-validation for cross-entropy method, Optimizing the Optimizers Workshop at Neural Information Processing Systems (NIPS), Barcelona, Spain, December 2016.
  51. J.M. Cashore, X. Zhao, A.A. Alemi, Y. Liu, and P.I. Frazier: Clustering via Content-Augmented Stochastic Blockmodels. (submitted)
  52. B. Chen and P.I. Frazier: The Bayesian Linear Information Filtering Problem, IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2016.
  53. P.I. Frazier and J. Wang: Bayesian optimization for materials design, Information Science for Materials Discovery and Design, Springer Series in Materials Science, Vol. 225, pp 45-75, 2016.
  54. W. Hu and P.I. Frazier: Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies, AISTATS 2016.
  55. S.N. Pallone and P.I. Frazier, S.G. Henderson: Coupled Bisection for Root Ordering, Operations Research Letters, 2016.
  56. B. Peherstorfer, K. Willcox, and M. Gunzburger: Optimal model management for multifidelity Monte Carlo estimation, SIAM Journal on Scientific Computing, Vol. 38, No. 5, pp. A3163-A3194, 2016.
  57. I.O. Ryzhov, P.I. Frazier, and W.B. Powell: A New Optimal Stepsize for Approximate Dynamic Programming, IEEE Transactions on Automatic Control, Vol. 60, no. 03, pp 743-758, 2015.
  58. T. Schnabel, T. Joachims, A. Swaminathan, and P.I. Frazier: Unbiased Concurrent Evaluation on a Budget. 2nd ACM International Conference on the Theory of Information Retrieval (ICTIR), 2016.
  59. J. Wu, J.G. Dai, and P.I. Frazier: Online Advertising Matching in the Large Market: Benefits of Clustering Advertisers. (submitted)
  60. J. Xie, P.I. Frazier, and S.E. Chick: Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs, Operations Research, 2016.
  61. P. Yang, K. Iyer, and P.I. Frazier: Mean Field Equilibria for Competitive Exploration in Resource Sharing Settings, WWW 2016.
  62. X. Zhao and P.I. Frazier: Exploration vs. Exploitation in the Information Filtering Problem. (submitted)
  63. M. Poloczek, J. Wang, and P.I. Frazier: Warm Starting Bayesian Optimization. Winter Simulation Conference, 2016.
  64. J. Wu and P.I. Frazier: The Parallel Knowledge Gradient Method for Batch Bayesian Optimization, Neural Information Processing Systems (NIPS), 2016.
  65. M. Poloczek and D. P. Williamson: An Experimental Evaluation of Fast Approximation Algorithms for the Maximum Satisfiability Problem. International Symposium on Experimental Algorithms (SEA), 2016.
  66. J.M. Cashore, L. Kumarga, and P.I. Frazier: Multi-Step Bayesian Optimization for One-Dimensional Feasibility Determination. (submitted)
  67. B. Chen and P.I. Frazier: Dueling Bandits with Dependent Arms. (submitted)
  68. B. Yang, C. Cardie, and P.I. Frazier: A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution, Transactions of the Association for Computational Linguistics, Vol. 3, pp 517-528, 2015.
  69. S.J. Gershman, P.I. Frazier, and D.M. Blei: Distance Dependent Infinite Latent Feature Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 2, pp 334-345, 2015.
  70. D. Singhvi, S. Singhvi, P.I. Frazier, S.G. Henderson, E. O'Mahony, D.B. Shmoys, and D.B. Woodard: Predicting Bike Usage for New York City's Bike Sharing System, AAAI-15 Workshop on Computational Sustainability, 2015.
  71. S. Toscano-Palmerin and P.I. Frazier: Asymptotic Validity of the Bayes-Inspired Indifference Zone Procedure: the Non-Normal Known Variance Case, Winter Simulation Conference, 2015.
  72. P. Rajan, W. Han, R. Sznitman, P.I. Frazier, and B.M. Jedynak: Bayesian Multiple Target Localization, International Conference on Machine Learning (ICML), 2015.


RT2: Goal-oriented reduced modeling

  1. P. Song, X.Q. Wang, and M.P. Mignolet: Uncertainty Management for the Stochastic Response of Uncertain Structures, AIAA SciTech AIAA Paper AIAA-2020-1419, Orlando, Florida, Jan.6-10, 2020.
  2. X.Q. Wang, P. Song, and M.P. Mignolet: Applications Of Multifidelity Reduced Order Modeling To Single And Multiphysics Nonlinear Structural Problems, Applications in Engineering Science, 2020 (also appeared in AIAA SciTech AIAA Paper AIAA-2020-2131, Orlando, Florida, Jan.6-10, 2020). (under review)
  3. P. Song: Uncertainty Modeling at the Elemental Level for Heated Structures, International Journal for Uncertainty Quantification, 2020. (under review)
  4. M.P. Mignolet, and C. Soize: Compressed Principal Component Analysis of Non-Gaussian Vectors, SIAM/ASA Journal on Uncertainty Quantification, 2020. (to appear)
  5. P. Song, X.Q. Wang, R. Murthy, A.K. Matney, and M.P. Mignolet: Nonlinear Geometric Thermoelastic Response of Structures with Uncertain Thermal and Structural Properties, AIAA Journal, Vol. 58, No. 8, pp. 3639-3652, 2020 (also appeared in proceedings of the AIAA SCITECH, Dallas, Texas, Jan. 9-13, 2017, AIAA 2017-0181).
  6. E. Qian, B. Kramer, B. Peherstorfer, and K. Willcox: Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems, Physica D: Nonlinear Phenomena, Volume 406, 132401, 2020.
  7. R. Swischuk, B. Kramer, C. Huang, and K. Willcox: Learning physics-based reduced-order models for a single-injector combustion process, AIAA Journal, Vol. 58, No. 6, pp. 2658-2672, 2020. (Also in Proceedings of 2020 AIAA SciTech Forum & Exhibition, Orlando FL, January, 2020)
  8. B. Kramer, and K. Willcox: Balanced Truncation Model Reduction for Lifted Nonlinear Systems. In Realization and Model Reduction of Dynamical Systems, Springer, 2020. (to appear)
  9. P. Song, X.Q. Wang and M.P. Mignolet: Multi-Fidelity Reduced Order Modeling of Representative Hypersonic Panel, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  10. P. Song, X.Q. Wang, and M.P. Mignolet: Nonlinear Reduced ROMs: Formulation and Applications, AIAA SciTech2019 AIAA-2019-1020, San Diego, California, Jan.7-11, 2019.
  11. P. Song, and M.P. Mignolet: Maximum Entropy-Based Uncertainty Modeling at the Elemental Level in Linear Structural and Thermal Problems, Computational Mechanics, Vol 64, No. 6, pp 1557–1566, 2019.
  12. P. Song, X.Q. Wang, and M.P. Mignolet: Maximum Entropy Structural-Thermal Uncertainty Modeling at the Finite Element Level,” Proceedings of the AIAA Science and Technology Forum and Exposition (SciTech2019) AIAA-2019-0443., San Diego, California, Jan.7-11, 2019.
  13. P. Song, and M.P. Mignolet: Maximum Entropy-based Uncertainty Modeling at the Finite Element Level, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  14. B. Peherstorfer, B. Kramer, and K. Willcox: Multifidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation, SIAM/ASA Journal on Uncertainty Quantification, Vol. 6, No. 2, pp. 737-761, 2018.
  15. P. Song, and M.P. Mignolet: Reduced order model-based uncertainty modeling of structures with localized response, Probabilistic Engineering Mechanics, Vol. 51, pp. 42-55, 2018. (also appeared in 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2017), Rhodes Island, Greece, 15–17 June 2017)
  16. B. Peherstorfer, B. Kramer, and K. Willcox: Combining multiple surrogate models to accelerate failure probability estimation with expensive high-fidelity models, Journal of Computational Physics, Vol. 341, pp. 61-75, 2017, https://doi.org/10.1016/j.jcp.2017.04.012.
  17. P. Song, X.Q. Wang, A. Matney, R. Murthy, and M.P. Mignolet: Nonlinear Geometric Thermoelastic Response of Structures with Uncertain Thermal and Structural Properties, AIAA SciTech AIAA Paper AIAA 2017-0181, Dallas, Texas, Jan. 9-13, 2017.
  18. B. Peherstorfer, K. Willcox: Dynamic data-driven model reduction: Adapting reduced models from incomplete data, Advanced Modeling and Simulation in Engineering Sciences, 3(11), Springer, 2016.
  19. P. Song, A. K. Matney, R. Murthy, X.Q. Wang, and M. P. Mignolet: Probabilistic Modeling Of Thermal Properties Of Hot Structures And Its Propagation To The Nonlinear Geometric Structural Response, Probabilistic Mechanics and Reliability Conference 2016, Vanderbilt University, Nashville, TN, May 22-25, 2016.
  20. P. Song, X.Q. Wang, M.P. Mignolet, and P.C. Chen: A Reduced Order Model-Based Nonlinear Damping Model: Formulation and Application to Post Flutter Aeroelastic Behavior, AIAA SciTech AIAA Paper AIAA 2016-1795, San Diego, California, Jan. 4-8, 2016,
  21. K. Li, and D. Allaire: A compressed sensing approach to uncertainty propagation for approximately additive functions, ASME 2016 International Design Engineering Technical Conferences, IDETC/CIE, 2016.


RT3: Managing coupling in multi-physics system

  1. A. Marques, R. Lam, A. Chaudhuri, M. Opgenoord, and K. Willcox: Multifidelity method for locating aeroelastic flutter boundaries, AIAA Journal, Vol. 58, No. 4, pp. 1772-1784, 2020 (also in 21st AIAA Non-Deterministic Approaches Conference (AIAA Scitech), San Diego, CA, January 2019, doi 10.2514/1.J058663).
  2. L. Cook, K. Willcox, and J. Jarrett: Design Optimization Using Multiple Dominance Relations, International Journal for Numerical Methods in Engineering, Vol. 121, Issue 11, pp. 2481-2502, 2020.
  3. Xiaosong Du, Ping He and Joaquim Martins: A B-Spline-based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization, AIAA SciTech MURI Special Session, Orlando, FL, January 2020.
  4. R. Baptista and M. Poloczek: Bayesian Optimization of Combinatorial Structures, To Appear in the Proc. of Thirty-fifth International Conference on Machine Learning (ICML), 2018.
  5. R. Baptista, Y. Marzouk, K. Willcox, and B. Peherstorfer: Optimal Approximations of Coupling in Multidisciplinary Models, AIAA Journal, Vol. 56, No. 6, pp. 2412-2428, 2018, https://dx.doi.org/10.2514/1.J056888. (An earlier version of this work appeared in AIAA paper 2017-1935, January 2017.)
  6. L.W. Cook, and J.P. Jarrett: Optimization Using Multiple Dominance Criteria for Aerospace Design Under Uncertainty, AIAA Journal, Vol. 56, No. 12, pp. 4965-4976, 2019.
  7. L.W. Cook, J.P. Jarrett, and K. Willcox: Generalized Information Reuse for Optimization Under Uncertainty with Non-Sample Average Estimators, International Journal for Numerical Methods in Engineering, Vol. 115, Issue 12, pp. 1457-1476, 2018.
  8. L.W. Cook, J.P. Jarrett, and K. Willcox: Extending Horsetail Matching for Optimization Under Probabilistic, Interval and Mixed Uncertainties, AIAA Journal, 2017. DOI: 10.2514/1.J056371.(An earlier version of this work appeared in 19th AIAA Non-Deterministic Approaches Conference (AIAA SciTech), January 2017.)
  9. S. Friedman, B. Isaac and D. Allaire: Efficient Decoupling of Multiphysics Systems for Uncertainty Propagation, 20th AIAA Non-Deterministic Approaches Conference (AIAA SciTech) MURI Special Session, Kissimmee, FL, January 2018.
  10. B. Isaac, S. Friedman and D. Allaire: Efficient Approximation of Coupling Variable Fixed Point Sets for Decoupling Multidisciplinary Systems, AIAA Journal, 2018. (submitted)
  11. R. Morrison, R. Baptista, and Y. Marzouk: Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting, In Advances In Neural Information Processing Systems (NIPS) 2017.
  12. S.F. Ghoreishi, and D. Allaire: Adaptive uncertainty propagation for coupled multidisciplinary systems, AIAA Journal, pp. 1–11, 2017.
  13. A. Chaudhuri, R. Lam, and K. Willcox: Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems, AIAA Journal, Vol. 56, No. 1, pp. 235-249, 2018, https://dx.doi.org/10.2514/1.J055678. (An earlier version of this work appeared in AIAA paper 2016-1442, January 2016.)
  14. J. P. Jasa and J. T. Hwang, and J. R. R. A. Martins: Open-source coupled aerostructural optimization using Python, Structural and Multidisciplinary Optimization, Vol. 57, No. 4, pp. 1815-1827, 2018.
  15. L. Jichao, M. A. Bouhlel, and J. R. R. A. Martins: A Data-based Approach for Fast Airfoil Analysis and Optimization, 19th AIAA/ISSMO Multidisciplinary Design Optimization: Metamodeling and Approximation Methods, 2018.
  16. J.T. Hwang, and J.R.R.A. Martins: A computational architecture for coupling heterogeneous numerical models and computing coupled derivatives, ACM Transactions on Mathematical Software (TOMS), Vol. 44, No. 4, pp. 1-39, 2018.
  17. S. Friedman, S.F. Ghoreishi, and D. Allaire: Quantifying the Impact of Different Model Discrepancy Formulations in Coupled Multidisciplinary Systems, 19th AIAA Non-Deterministic Approaches Conference, AIAA SCITECH, Grapevine, TX 2017.
  18. W.D. Thomison, and D. Allaire: A Model Reification Approach to Fusing Information from Multifidelity Information Sources, 19th AIAA Non-Deterministic Approaches Conference (AIAA SciTech), Grapevine, TX 2017.
  19. S. Friedman, and D. Allaire: Quantifying model discrepancy in coupled multi-physics systems, ASME 2016 International Design Engineering Technical Conferences, IDETC/CIE, 2016.
  20. S.F. Ghoreishi, and D. Allaire: Compositional uncertainty analysis via importance weighted Gibbs sampling for coupled multidisciplinary systems,18th AIAA Non-Deterministic Approaches Conference, AIAA SCITECH, San Diego, CA, January 2016.
  21. J.T. Hwang, and J.R.R.A. Martins: A fast, robust interpolant for scattered multivariate data using regularized minimal-energy tensor-product splines, ACM Transactions on Mathematical Software, 2016. (submitted)

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