Manuscripts

  1. J. Liao, X. Huan and D. Cooper (2025), Intelligent data collection for network discrimination in material flow analysis using Bayesian optimal experimental design. Available at arXiv:2504.13382. [preprint]
  2. T. E. Coons and X. Huan (2025), A multi-fidelity estimator of the expected information gain for Bayesian optimal experimental design. Available at arXiv:2501.10845. [preprint]
  3. J. Liao, X. Huan and D. Cooper (2025), Bayesian model selection for network discrimination and risk-informed decision making in material flow analysis. Available at arXiv:2501.05556. [preprint]
  4. H. Chen, G. Toth, Y. Chen, S. Zou, Z. Huang and X. Huan (2024), GeoDGP: One-hour ahead global probabilistic geomagnetic perturbation forecasting using deep Gaussian process. Available at ESSOAR. [preprint]
  5. A. Chakraborty, X. Huan and T. Catanach (2024), A likelihood-free approach to goal-oriented Bayesian optimal experimental design. Available at arXiv:2408.09582. [preprint]
  6. S. Zhong, W. Shen, T. Catanach and X. Huan (2024), Goal-oriented Bayesian optimal experimental design for nonlinear models using Markov chain Monte Carlo. Available at arXiv:2403.18072. [preprint]
  7. W. Shen, J. Dong and X. Huan (2023), Variational sequential optimal experimental design using reinforcement learning. Available at arXiv:2306.10430. [preprint] (Significantly updated in December 2024!)
  8. C. Huang, S. Srivastava, X. Huan and K. Garikipati (2023), FP-IRL: Fokker–Planck-based inverse reinforcement learning – A physics-constrained approach to Markov decision processes. Available at arXiv:2306.10407. [preprint]
  9. M. Han Veiga, X. Meng, O. Y. Gnedin, N. Y. Gnedin and X. Huan (2021), Reconstruction of the density power spectrum from quasar spectra using machine learning. Available at arXiv:2107.09082. [preprint]
  10. X. Huan and Y. M. Marzouk (2016), Sequential Bayesian optimal experimental design via approximate dynamic programming. Available at arXiv:1604.08320. [preprint]

Journal Papers

  1. H. Chen, N. Sachdeva, Z. Huang, B. van der Holst, W. B. Manchester IV, A. Jivani, S. Zou, Y. Chen, X. Huan and G. Toth (2025), Decent estimate of CME arrival time from a data-assimilated ensemble in the Alfvén Wave Solar atmosphere Model (DECADE-AWSoM), Space Weather 23(1), e2024SW004165. [pdf, preprint]
  2. C. Jacobsen, J. Dong, M. Khalloufi, X. Huan, K. Duraisamy, M. Akram and W. Liu (2025), Enhancing dynamical system modeling through interpretable machine learning augmentations: A case study in cathodic electrophoretic deposition, Data-Centric Engineering 6, e4. [pdf, preprint]
  3. J. Dong, C. Jacobsen, M. Khalloufi, M. Akram, W. Liu, K. Duraisamy and X. Huan (2025), Variational Bayesian optimal experimental design with normalizing flows, Computer Methods in Applied Mechanics and Engineering 433, 117457. [preprint]
  4. C. L. Lanaghan, O. Okia, T. Coons, S. K. Yadavalli, J. R. Palmer, M. Zhang, K. Hersh, M. Kodur, O. Trejo, S. P. Dunfield, M. D. Thouless, D. P. Fenning, X. Huan and N. P. Dasgupta (2024), Understanding process-structure relationships during lamination of halide perovskite interfaces, ACS Applied Materials & Interfaces 16(43), 58657–58667.
  5. X. Huan, J. Jagalur and Y. Marzouk (2024), Optimal experimental design: Formulations and computations, Acta Numerica 33, 715–840. [pdf, preprint] (See arXiv for a more up-to-date version!)
  6. J. Cohen, X. Huan and J. Ni (2024), Shapley-based explainable AI for clustering applications in fault diagnosis and prognosis, Journal of Intelligent Manufacturing 35, 4071–4086. [preprint]
  7. J. Hauth, C. Safta, X. Huan, R. G. Patel and R. E. Jones (2024), Uncertainty quantification of graph convolution neural network models of evolving processes, Computer Methods in Applied Mechanics and Engineering 429, 117195. [preprint]
  8. A. Thelen, X. Huan, N. Paulson, S. Onori, Z. Hu and C. Hu (2024), Probabilistic machine learning for battery health diagnostics and prognostics—Review and perspectives, npj Materials Sustainability 2, 14. [pdf]
  9. P. C. Kinnunen, K. K. Y. Ho, S. Srivastava, C. Huang, W. Shen, K. Garikipati, G. D. Luker, N. Banovic, X. Huan, J. J. Linderman and K. E. Luker (2024), Integrating inverse reinforcement learning into data-driven mechanistic computational models: A novel paradigm to decode cancer cell heterogeneity, Frontiers in Systems Biology 4, 1333760. [pdf]
  10. S. V. Modak, D. Pert, J. L. Tami, W. Shen, I. Abdullahi, X. Huan, A. J. McNeil, B. R. Goldsmith and D. G. Kwabi (2024), Substituent impact on quinoxaline performance and degradation in redox flow batteries, Journal of the American Chemical Society 146(8), 5173–5185.
  11. J. Cohen and X. Huan (2024), Uncertainty-aware explainable AI as a foundational paradigm for digital twins, Frontiers in Mechanical Engineering 9, 1329146. [pdf]
  12. Z. Chen, H. Maske, H. Shui, D. Upadhyay, M. Hopka, J. Cohen, X. Lai, X. Huan and J. Ni (2023), Stochastic deep Koopman model for quality propagation analysis in multistage manufacturing systems, Journal of Manufacturing Systems 71, 609–619. [preprint]
  13. V. Nemani, L. Biggio, X. Huan, Z. Hu, O. Fink, A. Tran, Y. Wang, X. Zhang and C. Hu (2023), Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial, Mechanical Systems and Signal Processing 205, 110796. [preprint]
  14. M. Morelli, J. Hauth, A. Guardone, X. Huan and B. Y. Zhou (2023), A rotorcraft in-flight ice detection framework using computational aeroacoustics and Bayesian neural networks, Structural and Multidisciplinary Optimization 66, 197. [pdf]
  15. W. Shen and X. Huan (2023), Bayesian sequential optimal experimental design for nonlinear models using policy gradient reinforcement learning, Computer Methods in Applied Mechanics and Engineering 416, 116304. [preprint]
  16. J. Cohen, X. Huan and J. Ni (2023), Fault prognosis of turbofan engines: Eventual failure prediction and remaining useful life estimation, International Journal of Prognostics and Health Management 14(2), 3486. [pdf, preprint]
  17. J. Dong, J. Liao, X. Huan and D. Cooper (2023), Expert elicitation and data noise learning for material flow analysis using Bayesian inference, Journal of Industrial Ecology 27(4), 1105–1122. [pdf, preprint]
  18. S. V. Modak, W. Shen, S. Singh, D. Herrera, F. Oudeif, B. R. Goldsmith, X. Huan and D. G. Kwabi (2023), Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques, Nature Communications 14, 3602. [pdf]
  19. T. Hossain*, W. Shen*, A. D. Antar, S. Prabhudesai, S. Inoue, X. Huan and N. Banovic (2023), A Bayesian approach for quantifying data scarcity when modeling human behavior via inverse reinforcement learning, ACM Transactions on Computer-Human Interaction 30(1), 8:1–8:27. [pdf] *Contributed equally
  20. S. Prabhudesai*, J. Hauth*, D. Guo, A. Rao, N. Banovic and X. Huan (2023), Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI, Frontiers in Computer Science 5, 1071174. [pdf] *Share first authorship
  21. A. Jivani, N. Sachdeva, Z. Huang, Y. Chen, B. van der Holst, W. Manchester, D. Iong, H. Chen, S. Zou, X. Huan and G. Toth (2022), Global sensitivity analysis and uncertainty quantification for background solar wind using the Alfvén wave solar atmosphere model, Space Weather 21(1), e2022SW003262. [pdf, preprint]
  22. Y. Yao, X. Huan and J. Capecelatro (2022), Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow, Journal of Aerosol Science 166, 106065. [preprint]
  23. R. Nagi, X. Huan and Y. C. Chen (2022), Bayesian inference of parameters in power system dynamic model using trajectory sensitivities, IEEE Transactions on Power Systems 37(2), 1253–1263. 
  24. S. Prabhudesai, N. C. Wang, V. Ahluwalia, X. Huan, J. R. Bapuraj, N. Banovic and A. Rao (2021), Stratification by tumor grade groups in a holistic evaluation of machine learning for brain tumor segmentation, Frontiers in Neuroscience 15, 740353. [pdf]
  25. B. Kochunas and X. Huan (2021), Digital twin concepts with uncertainty for nuclear power applications, Energies 14(14), 4235. [pdf]
  26. J. Hauth*, S. Jabri*, F. Kamran, E. W. Feleke, K. Nigusie, L. V. Ojeda, S. Handelzalts, L. Nyquist, N. B. Alexander, X. Huan, J. Wiens and K. H. Sienko (2021), Automated loss-of-balance event identification in older adults at risk of falls during real-world walking using wearable inertial measurement units, Sensors 21(14), 4661. [pdf] *Co-first authors
  27. C. Denamiel, X. Huan and I. Vilibić (2021), Conceptual design of extreme sea-level early warning systems based on uncertainty quantification and engineering optimization methods, Frontiers in Marine Science 8, 562. [pdf]
  28. Z. Wang, X. Huan and K. Garikipati (2021), Variational system identification of the partial differential equations governing microstructure evolution in materials: Inference over sparse and spatially unrelated data, Computer Methods in Applied Mechanics and Engineering 377, 113706. [preprint]
  29. Z. Wang, B. Wu, K. Garikipati and X. Huan (2020), A perspective on regression and Bayesian approaches for system identification of pattern formation dynamics, Theoretical and Applied Mechanics Letters 10(3), 188–194. [pdf, preprint]
  30. C. Denamiel, X. Huan, J. Šepić and I. Vilibić (2020), Uncertainty propagation using polynomial chaos expansions for exteme sea-level hazard assessment: The case of the eastern Adriatic meteotsunamis, Journal of Physical Oceanography 50(4), 1005–1021. 
  31. C. Denamiel, J. Šepić, X. Huan, C. Bolzer and I. Vilibić (2019), Stochastic surrogate model for meteotsunami early warning system in the eastern Adriatic Sea, Journal of Geophysical Research: Oceans 124(11), 8485–8499. 
  32. R. G. Ghanem, C. Soize, C. Safta, X. Huan, G. Lacaze, J. C. Oefelein and H. N. Najm (2019), Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds, Journal of Computational Physics 399, 108930. 
  33. K. Sargsyan, X. Huan and H. N. Najm (2019), Embedded model error representation for Bayesian model calibration, International Journal for Uncertainty Quantification 9(4), 365–394. [preprint]
  34. Z. Wang, X. Huan and K. Garikipati (2019), Variational system identification of the partial differential equations governing the physics of pattern-formation: Inference under varying fidelity and noise, Computer Methods in Applied Mechanics and Engineering 356, 44–74. [preprint]
  35. C. Soize, R. Ghanem, C. Safta, X. Huan, Z. P. Vane, J. C. Oefelein, G. Lacaze, H. N. Najm, Q. Tang and X. Chen (2019), Entropy-based closure for probabilistic learning on manifolds, Journal of Computational Physics 388, 518–533.
  36. P. Tsilifis, X. Huan, C. Safta, K. Sargsyan, G. Lacaze, J. C. Oefelein, H. N. Najm and R. G. Ghanem (2019), Compressive sensing adaptation for polynomial chaos expansions, Journal of Computational Physics 380, 29–47. [preprint]
  37. C. Soize, R. Ghanem, C. Safta, X. Huan, Z. P. Vane, J. C. Oefelein, G. Lacaze and H. N. Najm (2019), Enhancing model predictability for a scramjet using probabilistic learning on manifolds, AIAA Journal 57(1), 365–378. 
  38. D. Vuilleumier, X. Huan, T. Casey and M. G. Sjöberg (2018), Uncertainty assessment of octane index framework for stoichiometric knock limits of Co-Optima gasoline fuel blends, SAE International Journal of Fuels and Lubricants 11(3), 247–270. 
  39. X. Huan, C. Safta, K. Sargsyan, Z. P. Vane, G. Lacaze, J. C. Oefelein and H. N. Najm (2018), Compressive sensing with cross-validation and stop-sampling for sparse polynomial chaos expansions, SIAM/ASA Journal on Uncertainty Quantification 6(2), 907–936. [pdf, preprint]
  40. X. Huan, C. Safta, K. Sargsyan, G. Geraci, M. S. Eldred, Z. P. Vane, G. Lacaze, J. C. Oefelein and H. N. Najm (2018), Global sensitivity analysis and estimation of model error, toward uncertainty quantification in scramjet computations, AIAA Journal 56(3), 1170–1184. [preprint]
  41. M. Vohra, X. Huan, T. P. Weihs and O. M. Knio (2017), Design analysis for optimal calibration of diffusivity in reactive multilayers, Combustion Theory and Modelling 21(6), 1023–1049. [preprint]
  42. X. Huan and Y. M. Marzouk (2014), Gradient-based stochastic optimization methods in Bayesian experimental design, International Journal for Uncertainty Quantification 4(6), 479–510. [pdf, preprint]
  43. X. Huan and Y. M. Marzouk (2013), Simulation-based optimal Bayesian experimental design for nonlinear systems, Journal of Computational Physics 232(1), 288–317. [preprint]

Machine Learning Conference Papers

  1. S. Prabhudesai, L. Yang, S. Asthana, X. Huan, Q. V. Liao and N. Banovic (2023), Understanding uncertainty: How lay decision-makers perceive and interpret uncertainty in human-AI decision making, in Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI ’23), pp. 379–396. [pdf]

Conference Papers

  1. D. Séguin, X. Huan and Y. C. Chen (2024), A Bayesian method to infer parameters in power flow models using linear sensitivities, in 2024 IEEE Power & Energy Society General Meeting (PESGM). [preprint]
  2. L. Spicher, C. Bell, X. Huan and K. Sienko (2024), Exploring random forest machine learning for fetal movement detection using abdominal acceleration and angular rate data, in 2024 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 
  3. Z. Chen, H. Maske, D. Upadhyay, H. Shui, X. Huan and J. Ni (2024), Deep Koopman-based control of quality variation in multistage manufacturing systems, in 2024 American Control Conference (ACC), pp. 893–898. [preprint]
  4. J. Cohen, E. Byon and X. Huan (2023), To trust or not: Towards efficient uncertainty quantification for stochastic Shapley explanations, in Proceedings of the Asia Pacific Conference of the PHM Society 2023 4(1), paper R03–06. [pdf]
  5. B. Y. Zhou, L. P. Hanson, S. F. Pullin, B. Zang, J. Hauth and X. Huan (2023), Multi-fidelity propeller noise prediction using a data-driven approach, in AIAA AVIATION 2023 Forum, AIAA paper 2023–4184. [preprint]
  6. J. Liao, X. Huan, K. R. Haapala and D. R. Cooper (2023), A Bayesian approach to modeling unit manufacturing process environmental impacts using limited data with case studies on laser powder bed fusion cumulative energy demand, in Procedia CIRP 116, pp. 516–521. [pdf]
  7. B. Y. Zhou, L. P. Hanson, S. F. Pullin, B. Zang, J. Hauth and X. Huan (2022), A data-driven approach for enhancement of propeller performance prediction, in 28th AIAA/CEAS Aeroacoustics 2022 Conference, AIAA paper 2022–3106. [preprint]
  8. H. Tong, J. M. A. Hauth, X. Huan, B. Y. Zhou, N. R. Gauger, M. Morelli and A. Guardone (2022), Bayesian recurrent neural networks for monitoring rotorcraft icing from aeroacoustics time-series data, in AIAA Scitech 2022 Forum, AIAA paper 2022–2358. [preprint]
  9. A. Jivani, X. Huan, C. Safta, B. Y. Zhou and N. R. Gauger (2021), Uncertainty quantification for a turbulent round jet using multifidelity Karhunen–Loeve Expansions, in AIAA Scitech 2021 Forum, AIAA paper 2021–1367. [preprint]
  10. W. Shen, X. Huan, B. Y. Zhou and N. R. Gauger (2020), Towards design of airfoil pressure tap locations for real-time predictions under uncertainty using Bayesian neural networks, in AIAA Scitech 2020 Forum, AIAA paper 2020–0906. [preprint]
  11. B. Y. Zhou, N. R. Gauger, M. Morelli, A. Guardone, J. Hauth and X. Huan (2020), Development of a real-time in-flight ice detection system via computational aeroacoustics and Bayesian neural networks, in AIAA Scitech 2020 Forum, AIAA paper 2020–1638. [preprint]
  12. J. Hauth, X. Huan, B. Y. Zhou, N. R. Gauger, M. Morelli and A. Guardone (2020), Correlation effects in Bayesian neural networks for computational aeroacoustics ice detection, in AIAA Scitech 2020 Forum, AIAA paper 2020–1414. [preprint]
  13. B. Y. Zhou, N. R. Gauger, M. Morelli, A. Guardone, J. Hauth and X. Huan (2019), Towards a real-time in-flight ice detection system via computational aeroacoustics and Bayesian neural networks, in AIAA Aviation 2019 Forum, AIAA paper 2019–3103. [preprint]
  14. X. Huan, C. Safta, Z. P. Vane, G. Lacaze, J. C. Oefelein and H. N. Najm (2019), Uncertainty propagation using conditional random fields in large-eddy simulations of scramjet computations, in AIAA Scitech 2019 Forum, AIAA paper 2019–0724. [preprint]
  15. G. Geraci, F. Menhorn, X. Huan, C. Safta, Y. M. Marzouk, H. N. Najm and M. S. Eldred (2019), Progress in scramjet design optimization under uncertainty using simulations of the HIFiRE direct connect rig, in AIAA Scitech 2019 Forum, AIAA paper 2019–0725. [preprint]
  16. X. Huan, G. Geraci, C. Safta, M. S. Eldred, K. Sargsyan, Z. P. Vane, J. C. Oefelein and H. N. Najm (2018), Multifidelity statistical analysis of large eddy simulations in scramjet computations, in 2018 AIAA Non-Deterministic Approaches Conference, AIAA paper 2018–1180. [preprint]
  17. X. Huan, C. Safta, K. Sargsyan, G. Geraci, M. S. Eldred, Z. P. Vane, G. Lacaze, J. C. Oefelein and H. N. Najm (2017), Global sensitivity analysis and quantification of model error for large eddy simulation in scramjet design, in 19th AIAA Non-Deterministic Approaches Conference, AIAA paper 2017–1089. [preprint]
  18. M. E. Gharamti, Y. M. Marzouk, X. Huan and I. Hoteit (2015), A greedy approach for placement of subsurface aquifer wells in an ensemble filtering framework, in First International Conference on Dynamic Data-Driven Environmental Systems Science (DyDESS 2014), pp. 301–309. 
  19. X. Huan and Y. M. Marzouk (2011), Optimal Bayesian experimental design for combustion kinetics, in 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, AIAA paper 2011–513. [preprint]
  20. X. Huan, J. E. Hicken and D. W. Zingg (2009), Interface and boundary schemes for high-order methods, in 19th AIAA Computational Fluid Dynamics, AIAA paper 2009–3658.