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Data-Driven Dynamics

Learning dynamical systems and temporal evolution directly from sparse, noisy, or indirect data using models that capture multi-scale physical processes and underlying latent structure.

Neural Dynamic Modes: Computational Imaging of Dynamical Systems from Sparse Observations
arXiv 2025 SaraerToosi, Tu et al.
Effective Resistivity in Relativistic Reconnection: A Prescription Based on Fully Kinetic Simulations
The Astrophysical Journal Letters 2025 Moran et al.
Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements
Proc. IEEE/CVF ICCV 2021 Levis et al.
Statistical Tomography of Microscopic Life
Proc. IEEE CVPR 2018 Levis et al.
Date Title First Authors Venue
Jul 2025 Neural Dynamic Modes: Computational Imaging of Dynamical Systems from Sparse Observations
Ali SaraerToosi, Renbo Tu, Kamyar Azizzadenesheli, Aviad Levis
SaraerToosi, Tu et al. arXiv
Jan 2025 Effective Resistivity in Relativistic Reconnection: A Prescription Based on Fully Kinetic Simulations
Abigail Moran, Lorenzo Sironi, Aviad Levis, Bart Ripperda, Elias R. Most, Sebastiaan Selvi
Moran et al. The Astrophysical Journal Letters
Oct 2021 Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements
Aviad Levis, Daeyoung Lee, Joel A. Tropp, Charles F. Gammie, Katherine L. Bouman
Levis et al. Proc. IEEE/CVF ICCV
2018 Statistical Tomography of Microscopic Life
Aviad Levis, Yoav Y. Schechner, Ronen Talmon
Levis et al. Proc. IEEE CVPR