A Caltech Library Service

Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements

Levis, Aviad and Lee, Daeyoung and Tropp, Joel A. and Gammie, Charles F. and Bouman, Katherine L. (2021) Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE , Piscataway, NJ, pp. 2320-2329. ISBN 978-1-6654-2812-5.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


We develop an approach to recover the underlying properties of fluid-dynamical processes from sparse measurements. We are motivated by the task of imaging the stochastically evolving environment surrounding black holes, and demonstrate how flow parameters can be estimated from sparse interferometric measurements used in radio astronomical imaging. To model the stochastic flow we use spatio-temporal Gaussian Random Fields (GRFs). The high dimensionality of the underlying source video makes direct representation via a GRF’s full covariance matrix intractable. In contrast, stochastic partial differential equations are able to capture correlations at multiple scales by specifying only local interaction coefficients. Our approach estimates the coefficients of a space-time diffusion equation that dictates the stationary statistics of the dynamical process. We analyze our approach on realistic simulations of black hole evolution and demonstrate its advantage over state-of-the-art dynamic black hole imaging techniques.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Levis, Aviad0000-0001-7307-632X
Tropp, Joel A.0000-0003-1024-1791
Gammie, Charles F.0000-0001-7451-8935
Bouman, Katherine L.0000-0003-0077-4367
Additional Information:© 2021 IEEE. The authors would like to thank George Wong for his help with GRMHD simulations. AL is supported by the Zuckerman and Viterbi postdoctoral fellowships. This work was supported by NSF award 1935980: “Next Generation Event Horizon Telescope Design,” and Beyond Limits, and NSF awards 1743747, 1716327, and 2034306, XSEDE allocation TG-AST170024, and TACC Frontera LSCP AST20023. JAT was supported by ONR BRC Award N00014-18-1-2363 and NSF FRG Award 1952735.
Funding AgencyGrant Number
Zuckerman STEM Leadership ProgramUNSPECIFIED
Viterbi fellowshipUNSPECIFIED
Office of Naval Research (ONR)N00014-18-1-2363
Record Number:CaltechAUTHORS:20220307-188412000
Persistent URL:
Official Citation:A. Levis, D. Lee, J. A. Tropp, C. F. Gammie and K. L. Bouman, "Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements," 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2320-2329, doi: 10.1109/ICCV48922.2021.00234
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113765
Deposited By: George Porter
Deposited On:08 Mar 2022 16:17
Last Modified:08 Mar 2022 16:17

Repository Staff Only: item control page