Samuel Rudy

Massachusetts Institute of Technology — United States

Physics and Astronomy · Physical Sciences

11h-index2.1kcitations26works2.02yr avg
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Research Topics

Model Reduction and Neural Networks(12), Probabilistic and Robust Engineering Design(8), Fluid Dynamics and Vibration Analysis(7), Fluid Dynamics and Turbulent Flows(6), Wind and Air Flow Studies(5)

Publications26 total

Improving predictions of vortex induced vibrations via generalizable hydrodynamic databases across several current incidence angles
Journal of Fluids and Structures·2024· 2 cited
Physics-based Data-informed Prediction of Vertical, Catenary, and Stepped Riser Vortex-induced Vibrations
International Journal of Offshore and Polar Engineering·2023· 2 cited
Output-weighted and relative entropy loss functions for deep learning precursors of extreme events
Physica D Nonlinear Phenomena·2022· 17 cited
Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors
Ocean Engineering·2022· 16 cited
Optimized parametric hydrodynamic databases provide accurate response predictions and describe the physics of vortex-induced vibrations
Journal of Fluids and Structures·2022· 7 cited
Prediction of Intermittent Fluctuations from Surface Pressure Measurements on a Turbulent Airfoil
AIAA Journal·2022· 11 cited
Output-weighted and relative entropy loss functions for deep learning precursors of extreme events
arXiv (Cornell University)·2021Open Access
Prediction of Intermittent Fluctuations from Surface Pressure Measurements on a Turbulent Airfoil
arXiv (Cornell University)·2021Open Access
From Data to Assessment Models, Demonstrated through a Digital Twin of Marine Risers
Offshore Technology Conference·2021· 11 citedOpen Access
Learning Optimal Parametric Hydrodynamic Database for Vortex-Induced Crossflow Vibration Prediction of Both Freely-Mounted Rigid and Flexible Cylinders
The 31st International Ocean and Polar Engineering Conference·2021· 2 cited
Learning Optimal Parametric Hydrodynamic Database for Vortex-Induced Crossflow Vibration Prediction
arXiv (Cornell University)·2021· 5 citedOpen Access
Sparse methods for automatic relevance determination
Physica D Nonlinear Phenomena·2021· 18 citedOpen Access
Other files
Figshare·2021Open Access
A Data-driven Surrogate Model for Work Computation of a Periodically Forced Half-Sarcomere
2020Open Access
Smoothing and parameter estimation by soft-adherence to governing equations
Journal of Computational Physics·2019· 12 citedOpen Access
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
Journal of Computational Physics·2019· 178 citedOpen Access
Data-driven identification of parametric partial differential equations
IRIS Research product catalog (Sapienza University of Rome)·2019· 291 citedOpen Access
Computational methods for system identification and data-driven forecasting
ResearchWorks at the University of Washington (University of Washington)·2019Open Access
Data-Driven Identification of Parametric Partial Differential Equations
SIAM Journal on Applied Dynamical Systems·2019· 19 citedOpen Access
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
arXiv (Cornell University)·2018· 4 citedOpen Access
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Frequent Co-authors

Themistoklis P. Sapsis(11), Steven L. Brunton(7), J. Nathan Kutz(7), Dixia Fan(7), José del Águila Ferrandis(6), Michael S. Triantafyllou(6), Alessandro Alla(3), Andreas P. Mentzelopoulos(3), Bart Bogaerts(2), Joshua L. Proctor(1), Ehsan Kharazmi(1), Zhi‐Cheng Wang(1), Themis Sapsis(1), George Em Karniadakis(1), Pedro D. Maia(1), Nathan Kutz(1), Michael Triantafyllou(1), Edvard Ronglan(1), Mónica Arrozola David(1), Z. Jacek(1)