Publications

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

Preprints

  1. Gorodetsky, A. A., Jakeman, J. D., and Geraci, G. "MFNets: Learning network representations for multifidelity surrogates." 2020, http://arxiv.org/abs/2008.02672
  2. 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
  3. Kachar, K.G., Gorodetsky, A. A., Dynamic Multi-agent assignment via discrete optimal transport. 2020, arXiv preprint https://arxiv.org/abs/1910.10748 local hosted link

Peer-reviewed publications

Journal articles

  1. Galioto, N., Gorodetsky, A.A. "Bayesian system ID: optimal management of parameter, model, and measurement uncertainty." Nonlinear Dyn (2020). https://doi.org/10.1007/s11071-020-05925-8 ArXiv
  2. Gorodetsky, A. A., Jakeman, J.D., Geraci, G., Eldred, M.S., "MFNETS: multifidelity data-driven networks for Bayesian learning and prediction." Accepted, International Journal of Uncertainty Quantification 2020 local copy
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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. Galioto, N. Gorodetsky, A. A., "Bayesian Identification of Hamiltonian Dynamics from Symplectic Data." Conference on Decision and Control (CDC), 2020. local copy
  2. 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
  3. 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
  4. Kaijun, H., 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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

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

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, Alex Gorodetsky, goroda@umich.edu License: CC BY-SA 4.0