Daniel Gurevich

Daniel Gurevich

Publications

Author profile on Google Scholar.

In preparation

  1. D. R. Gurevich and A. Gaonkar, “Deductive synthesis of physical models by iterative sparse regression”.

  2. M. R. Golden, D. R. Gurevich, and R. O. Grigoriev, “Modeling and troubleshooting numerical simulations of spatially extended systems by sparse tensor regression”.

  3. M. Ugliotti, B. Choi, M. Reynoso, D. R. Gurevich, R. O. Grigoriev, “Physics-informed data-driven inference of an interpretable equivariant model of incompressible fluid turbulence”.

Preprints

  1. B. Choi, M. Ugliotti, M. Reynoso, D. R. Gurevich, R. O. Grigoriev, “Data-driven modeling of multiscale phenomena with applications to fluid turbulence,” 2025. (arXiv) (code)

Publications

  1. D. R. Gurevich, M. R. Golden, P. A. K. Reinbold, and R. O. Grigoriev, “Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER),” J. Fluid Mech 996, A25 (2024). (DOI) (arXiv) (code)

  2. D. R. Gurevich, D. Goswami, C. L. Fefferman, and C. W. Rowley, “Optimal Control with Learning on the Fly: System with Unknown Drift,” L4DC, PMLR 168, 870-880 (2022). (proceedings) (arXiv)

  3. P. A. Reinbold, D. R. Gurevich, and R. O. Grigoriev, “Using noisy or incomplete data to discover models of spatiotemporal dynamics,” Physical Review E 101, 010203 (2020). (DOI) (arXiv) (code)

  4. D. R. Gurevich, P. A. Reinbold, and R. O. Grigoriev, “Robust and optimal sparse regression for nonlinear PDE models,” Chaos 29, 103113 (2019). (paper) (DOI) (code)

  5. D. R. Gurevich and R. O. Grigoriev, “Robust approach for rotor mapping in cardiac tissue,” Chaos 29, 053101 (2019). (DOI) (arXiv) (code)

  6. D. R. Gurevich, C. Herndon, I. Uzelac, F. H. Fenton, and R. O. Grigoriev, “Level-set method for robust analysis of optical mapping recordings of fibrillation,” Computing in Cardiology 44, 197-427 (2017). (paper) (code)

  7. J. Barnett, D. R. Gurevich, and R. O. Grigoriev, “Streamwise localization of traveling wave solutions in channel flow,” Physical Review E 95, 033124 (2017). (DOI) (arXiv)