Publications

If you have trouble accessing any of these manuscripts, please email me asking for a copy.

Preprints

  1. Dixon, T. O., Warner, J. E., Bomarito, G. F., & Gorodetsky, A. A. (2023). Covariance Expressions for Multi-Fidelity Sampling with Multi-Output, Multi-Statistic Estimators: Application to Approximate Control Variates. arXiv preprint arXiv:2310.00125. arxiv preprint
  2. Aksoy, Doruk, David J. Gorsich, Shravan Veerapaneni, and Alex A. Gorodetsky. "An Incremental Tensor Train Decomposition Algorithm." arXiv preprint arXiv:2211.12487 (2022). local_copy
  3. De, S., Salehi, H. and Gorodetsky, A., "Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries." 2020, arXiv preprint https://arxiv.org/abs/2006.04295, local hosted link

Peer-reviewed publications

Journal articles

  1. Zeng, X., Geraci, G., Eldred, M.S., Jakeman, J.D., Gorodetsky, A.A., and Ghanem, R. "Multifidelity uncertainty quantification with models based on dissimilar parameters." Computer Methods in Applied Mechanics and Engineering 415 (2023): 116205 https://doi.org/10.1016/j.cma.2023.116205 arxiv preprint
  2. Salehi, H., Gorodetsky, A. A., Solhmirzaei, R., and Jiao, P. "High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition." Engineering Applications of Artificial Intelligence 125 (2023): 106659. https://doi.org/10.1016/j.engappai.2023.106659
  3. Yang, H., Fujii, Y., Wang, K. W., and Gorodetsky, A. A. "Control Variate Polynomial Chaos: Optimal Fusion of Sampling and Surrogates for Multifidelity Uncertainty Quantification." International Journal of Uncertainty Quantification, 13(3), (2023) : 69-100. 10.1615/Int.J.UncertaintyQuantification.2022043638 arxiv preprint
  4. Gorodetsky, A. A., Safta, S., and Jakeman, J. D. "Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning. " Journal of Machine Learning Research (JMLR), 23(143), (2022) : 1–29, jmlr_copy, local_copy
  5. Pham, T., and Gorodetsky, A. A. "Ensemble approximate control variate estimators: Applications to multi-fidelity importance sampling." SIAM Journal of Uncertainty Quantification (JUQ), 10(3), (2022) : 1250–1292, https://doi.org/10.1137/21M1390426, local copy
  6. Jakeman, J. D., Friedman, S., Eldred, M., Tamellini, L., Gorodetsky, A. A., Allaire, D. "Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems." International Journal of Numerical Methods in Engineering (IJNME) (2022): 1-31, https://doi.org/10.1002/nme.6958 local copy
  7. Yang, H. , Gorodetsky, A. A., Fujii, Y., Wang, K-W. "A Polynomial-Chaos-Based Multifidelity Approach to the Efficient Uncertainty Quantification of Online Simulations of Automotive Propulsion Systems." Journal of Computational and Nonlinear Dynamics 17(5) (2022): 051012. https://doi.org/10.1115/1.4053559
  8. Soley, B., Bergold, P., Gorodetsky, A. A., Batista, V.S., "Functional Tensor-Train Chebyshev Method for Multidimensional Quantum Dynamics Simulations." Journal of Chemical Theory and Computation 18:1 (2022): 25-36 https://doi.org/10.1021/acs.jctc.1c00941 local copy
  9. Kachar, K.G., Gorodetsky, A. A., "Dynamic Multi-agent assignment via discrete optimal transport." IEEE Transactions on Control of Network Systems, (2022), https://doi.org/10.1109/TCNS.2022.3141024 https://arxiv.org/abs/1910.10748 local copy
  10. Aksoy, D., Alben, S., Deegan, R. D., Gorodetsky, A. A. "Inverse Design of Self-Oscillatory Gels through Deep Learning.", Neural Computing and Applications, (2022). https://doi.org/10.1007/s00521-021-06788-9, local copy, supplementary material, code on bitbucket.
  11. Gorodetsky, A.A., Jakeman, J.D., Geraci, G., "MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources." Comput Mechanics (2021). https://doi.org/10.1007/s00466-021-02042-0, http://arxiv.org/abs/2008.02672
  12. Galioto, N., Gorodetsky, A.A. "Bayesian system ID: optimal management of parameter, model, and measurement uncertainty." Nonlinear Dynamics (2020). https://doi.org/10.1007/s11071-020-05925-8 ArXiv
  13. Gorodetsky, A. A., Jakeman, J.D., Geraci, G., Eldred, M.S., "MFNETS: multifidelity data-driven networks for Bayesian learning and prediction." International Journal of Uncertainty Quantification, 10, (2020) : 595–622. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2020032978 local copy
  14. Gorodetsky, A. A., Geraci, G., Eldred M. S., Jakeman, J. "A generalized approximate control variate framework for multifidelity uncertainty quantification." Journal of Computational Physics, 408, (2020): 109257. https://doi.org/10.1016/j.jcp.2020.109257 local copy
  15. Jakeman, J., Eldred, M. S., Geraci, G., Gorodetsky, A.A. "Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis." International Journal for Numerical Methods in Engineering, 121 (2019) : 1314 – 1343. https://doi.org/10.1002/nme.6268
  16. Gorodetsky, A. A., Karaman, S., and Marzouk, Y., "A continuous analogue of the tensor-train decomposition." Computer Methods in Applied Mechanics and Engineering 347 (2019): 59-84. https://doi.org/10.1016/j.cma.2018.12.015
  17. Alben, S., Gorodetsky, A. A., Kim, D., Deegan, R. D. "Semi-implicit methods for the dynamics of elastic sheets." Journal of Computational Physics, 399 (2019): 108952. https://doi.org/10.1016/j.jcp.2019.108952
  18. Wildey, T., Gorodetsky, A.A., Belme, A.C., Shadid, J. N., "Robust Uncertainty Quantification using reponse surface approximations of discontinuous functions" International Journal of Uncertainty Quantification, 9:5 (2019): 415-437. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2019026974 local copy
  19. Kramer, B., and Gorodetsky, A. "System identification via CUR-factored Hankel approximation." SIAM Journal on Scientific Computing, 40.2 (2018): A848-A866. https://doi.org/10.1137/17M1137632 local copy
  20. Gorodetsky, A. A., and Jakeman, J. D., "Gradient-based optimization for regression in the functional tensor-train format." Journal of Computational Physics 374 (2018): 1219-1238. https://doi.org/10.1016/j.jcp.2018.08.010
  21. Gorodetsky A. A., Karaman, S., and Marzouk Y.M. "High-dimensional stochastic optimal control using continuous tensor decompositions." International Journal of Robotics Research, 37.2-3 (2018): 340-377. https://doi.org/10.1177/0278364917753994
  22. Gorodetsky, A. A., and Marzouk, Y., "Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation" SIAM/ASA Journal on Uncertainty Quantification (2016): 4:1, 796-828. https://doi.org/10.1137/15M1017119
  23. Gorodetsky, A. A., and Marzouk, Y., "Efficient Localization of Discontinuities in Complex Computational Simulations," SIAM Journal on Scientific Computing (2014): 36:6, A2584-A2610. https://doi.org/10.1137/140953137

Conference publications

  1. Dixon, T. and Gorodetsky, A. A. "A Bi-fidelity Strategy for Optimization under Uncertainty with Applications to Aircraft Trajectory Optimization," AIAA 2024-1025. AIAA SCITECH 2024 Forum. January 2024. https://doi.org/10.2514/6.2024-1025
  2. Allen, M., Marks, T., Eckels, J., Gorodetsky, A.A., and Jorns. B. "Optimal Experimental Design for Inferring Anomalous Electron Transport in a Hall Thruster," AIAA 2024-2164. AIAA SCITECH 2024 Forum. January 2024. https://doi.org/10.2514/6.2024-2164
  3. Lipscomb, C., Boyd, I.D., Hansson, K.B., Eckels, J. and Gorodetsky, A.A.. "Simulation of Vacuum Chamber Pressure Distribution with Surrogate Modeling and Uncertainty Quantification," AIAA 2024-2369. AIAA SCITECH 2024 Forum. January 2024. https://doi.org/10.2514/6.2024-2369
  4. Eckels, J., Whittaker, C. B., Jorns, B., Gorodetsky, A. A. "Optimal experimental design to learn reduced-fidelity models for porous electrosprays". AIAA Scitech 2023 Forum , National Harbor, MD, United States of America, 2023. https://doi.org/10.2514/6.2023-0066
  5. Zeng, X., Geraci, G., Gorodetsky, A. A., Jakeman, J., Eldred, M. S., Ghanem, R. "Improving Bayesian networks multifidelity surrogate construction with basis adaptation". AIAA Scitech 2023 Forum , 2023. https://doi.org/10.2514/6.2023-0917
  6. McKenna, C., Gorodetsky, A. A. "Online parameter estimation within trajectory optimization for dynamic soaring". AIAA Scitech 2023 Forum , 2023. https://doi.org/10.2514/6.2023-1482
  7. Thompson, M., Geraci, G., Bomarito, G., Warner, J, Leser P., Leser, W., Eldred, M., Jakeman, J., Gorodetsky, A. A. "Strategies for Automation of Model Tuning in Multi-fidelity Trajectory Uncertainty Propagation". AIAA Scitech 2023 Forum, 2023. https://doi.org/10.2514/6.2023-1481
  8. Yang, H., Fujii, Y., Zhang, Y., Haria, H., Devendran, R.S., Saini, A., Gorodetsky, A. A and Wang, K.W., "Uncertainty Quantification of Wet Clutch Actuator Behaviors in P2 Hybrid Engine Restart Process (No. 2022-01-0652)." (2022) SAE Technical Paper.
  9. Bomarito, G., Geraci, G., Warner, J., Leser, P., Leser, W., Eldred, M. S., and Gorodetsky, A. "Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning." AIAA SCITECH 2022 Forum., San Diego, CA, USA, January 3-7, 2022. https://doi.org/10.2514/6.2022-1099
  10. Sharma, H., Galioto, N., Gorodetsky, A.A. and Kramer, B., 2022. "Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models." Conference on Decision and Control., Cancun, Mexico, December 6-9, 2022. arxiv
  11. Whittaker, C.B., Eckels, J., Gorodetsky, A.A., and Jorns, B. A. "A Moment-Based Model of Multi-Site Emission for Porous Electrosprays." International Electric Propulsion Conference (IEPC)., Boston, MA, USA, June 19-23, 2022. electricrocket.org, local_copy
  12. Allen, M. A., Eckels, J., Byrne, M. P., Gorodetsky, A. A., Jorns, B.A. "Application of Optimal Experimental Design to Characterize Pressure Related Facility Effects in a Hall Thruster." International Electric Propulsion Conference (IEPC)., Boston, MA, USA, June 19-23, 2022. electricrocket.org, local_copy
  13. Hurley, W., Marks, T., Gorodetsky, A. A., Jorns, B.A. "Application of Optimal Experimental Design to Characterize Pressure Related Facility Effects in a Hall Thruster." International Electric Propulsion Conference (IEPC)., Boston, MA, USA, June 19-23, 2022. electricrocket.org, local_copy
  14. Whittaker, C.B., Gorodetsky, A.A., and Jorns, B. A. "Model Inference from Electrospray Thruster Array Tests." AIAA SCITECH 2022 Forum., San Diego, CA, USA, January 3-7, 2022. https://doi.org/10.2514/6.2022-0041
  15. Eckels, J.D., Whittaker, C.B., Gorodetsky, A.A., Jorns, B. A., St. Peter, B., and Dressler, R. A., "Simulation-based surrogate methodology of electric field for electrospray emitter geometry design and uncertainty quantification." International Electric Propulsion Conference (IEPC)., Boston, MA, USA, June 19-23, 2022. electricrocket.org, local_copy
  16. Walker, M.L., Lev, D., Saeedifard, M., Jorns, B., Foster, J., Gallimore, A.D., Gorodetsky, A., Rovey, J.L., Chew, H.B., Levin, D., Williams, J.D., Yalin, A., Wirz, R.E., Marian, J., Boyd, I., Hara, K, and Lemmer, K. "Overview of the Joint AdvaNced PropUlsion InStitute (JANUS)." International Electric Propulsion Conference (IEPC)., Boston, MA, USA, June 19-23, 2022. electricrocket.org, local_copy
  17. Wirz, R.E., Gorodetsky, A. A., Jorns, B., and Walker, M.L., "Predictive Engineering Model for Life and Performance Assessment of High-Power Electric Propulsion Systems." International Electric Propulsion Conference (IEPC)., Boston, MA, USA, June 19-23, 2022. local_copy
  18. Ji, X., Molnar, T. G., Gorodetsky, A. A., Orosz, G. "Bayesian Inference for Time Delay Systems with Application to Connected Automated Vehicles." 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). Indianapolis, Indiana, USA, September 19 - 22, 2021. local copy
  19. Chen, B., Tandon, S., Gorsich, D., Gorodetsky, A., Veerapaneni, S. "Behavioral Cloning in Atari Games Using a Combined Variational Autoencoder and Predictor Model." IEEE Conference on Evolutionary Computation, Virtual, June 28 - July 1, 2021. local copy
  20. Gorodetsky, A. A., Whittaker, C.B., Szulman, A., Jorns, B. "Robust Design of Electrospray Emitters." AIAA Propulsion and Energy 2021 Forum. Virtual, August 11-13, 2021. local copy
  21. Yang, H. , Gorodetsky, A. A., Fujii, Y., Wang, K-W. "Multifidelity Uncertainty Quantification for Online Simulations of Automotive Propulsion Systems.", Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2021. Virtual, August 17-20, 2021. local copy
  22. Galioto, N. Gorodetsky, A. A., "A new objective for identification of partially observed linear time-invariant dynamical systems from input-output data." Learning for Dynamics and Control (L4DC), Virtual, June 7-8, 2021. PMLR copy, local_copy
  23. Galioto, N. Gorodetsky, A. A., "Bayesian Identification of Hamiltonian Dynamics from Symplectic Data." Conference on Decision and Control (CDC), 2020. local copy
  24. Yang, H. Kidambi, N., Fujii, Y., Gorodetsky, A., Wang, K-W. "Uncertainty Quantification Using Generalized Polynomial Chaos for Online Simulations of Automotive Propulsion Systems." American Control Conference (ACC) 2020. pp. 295-300. IEEE, 2020. https://doi.org/10.23919/ACC45564.2020.9147870 local copy
  25. Baskar, D., Gorodetsky, A. A., "Simulated Wind-field Dataset for Testing Energy Efficient Path-Planning Algorithms for UAVs in Urban Environment." 2020 AIAA Aviation Form, 2020. https://doi.org/10.2514/6.2020-2920
  26. He, K., Wang, J. and Gorodetsky, A. A., "Uncertainty Analysis of Trajectory Tracking for Autonomous Dynamic Soaring." AIAA Scitech 2020 Forum. 2020. https://doi.org/10.2514/6.2020-0908 local copy
  27. Whittaker, C. B., Gorodetsky, A., and Jorns, B. "Quantifying Uncertainty in the Scaling Laws of Porous Electrospray Emitters." AIAA Propulsion and Energy 2020 Forum. 2020. local_copy
  28. Jorns, B., Gorodetsky, A., Lasky, I. Kimber, A., Dahl P., St. Peter, B., Dressler, R. "Uncertainty Quantification of Electrospray Thruster Array Lifetime." 36th International Electric Propulsion Conference, University of Vienna, Austria, September 15 – 20, 2019. http://electricrocket.org/2019/317.pdf local copy
  29. Geraci, G., Eldred, M. S., Gorodetsky, A., and Jakeman, J. "Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA SEQUOIA project." AIAA Scitech Forum January 2019. https://doi.org/10.2514/6.2019-0722 local copy
  30. Geraci, G., Eldred, M.S., Gorodetsky, A. A., Jakeman J. "Leveraging Active Subspaces for Efficient Multifidelity Uncertainty Quantification." ECCM-ECCFD 2018, Glasgow, Scotland, UK 2018. local_copy
  31. Sayre-McCord, R. T., Guerra, W., Antonini, A., Arneberg, J., Brown, A., Cavalheiro,G., Fang, Y., Gorodetsky, A., McCoy, D., Quilter, S., Riether, F., Tal, E., Terzioglu, Y., Carlone, L., Karaman, S. "Visual-inertial navigation algorithm development using photorealistic camera simulation in the loop." Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018. https://doi.org/10.1109/ICRA.2018.8460692
  32. Eldred, M. S., Geraci, G., Gorodetsky, A., and Jakeman, J. "Multilevel-Multidelity Approaches for Forward UQ in the DARPA SEQUOIA project." 2018 AIAA Non-Deterministic Approaches Conference. 2018. https://doi.org/10.2514/6.2018-1179
  33. Tal, E., Gorodetsky, A. and Karaman, S., 2018, June. Continuous tensor train-based dynamic programming for high-dimensional zero-sum differential games. In 2018 Annual American Control Conference (ACC) (pp. 6086-6093). IEEE. https://10.23919/ACC.2018.8431472
  34. Gorodetsky, A. A., Karaman, S., and Marzouk, Y. M., "Low-rank tensor integration for Gaussian filtering of continuous time nonlinear systems," 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, VIC, 2017, pp. 2789-2794. https://10.1109/CDC.2017.8264064
  35. Alora, J. I., Gorodetsky, A. A., Karaman, S., Marzouk, Y. and Lowry, N., "Automated synthesis of low-rank control systems from sc-LTL specifications using tensor-train decompositions," 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, 2016, pp. 1131-1138, https://doi.org/10.1109/CDC.2016.7798419
  36. Gorodetsky, A.A., Karaman, S. and Marzouk, Y.M., 2015, July. Efficient High-Dimensional Stochastic Optimal Motion Control using Tensor-Train Decomposition. In Robotics: Science and Systems. https://doi.org/10.15607/RSS.2015.XI.015

PhD Thesis

  1. Gorodetsky, Alex Arkady. Continuous low-rank tensor decompositions, with applications to stochastic optimal control and data assimilation. Diss. Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108918
  2. Yang, Hang. A surrogate-based variance reduction approach to multifidelity uncertainty quantification — with applications to automotive systems. Diss. University of Michigan, 2022

S.M Thesis

Gorodetsky, Alex Arkady. A learning method for the approximation of discontinuous functions for stochastic simulations. Diss. Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76101


Copyright (c) 2020-2023, Alex Gorodetsky, goroda@umich.edu License: CC BY-SA 4.0