187 citations · 227 across the 6 of their papers we have counts for
8 papers
CO-STAR: Conceptualisation of Stereotypes for Analysis and Reasoning
Teyun Kwon, Anandha Gopalan
Warning: this paper contains material which may be offensive or upsetting. While much of recent work has focused on the detection of hate speech and overtly offensive content, very…
Prediction of Intermittent Fluctuations from Surface Pressure Measurements on a Turbulent Airfoil
Samuel H. Rudy, Themistoklis P. Sapsis
This work studies the effectiveness of several machine learning techniques for predicting extreme events occurring in the flow around an airfoil at low Reynolds. For certain Reynol…
Learning Optimal Parametric Hydrodynamic Database for Vortex-Induced Crossflow Vibration Prediction
Samuel Rudy, Dixia Fan, Jose del Aguila Ferrandis +2
The Vortex-induced vibration (VIV) prediction of long flexible cylindrical structures relies on the accuracy of the hydrodynamic database constructed via rigid cylinder forced vibr…
Sparse Methods for Automatic Relevance Determination
Samuel H. Rudy, Themistoklis P. Sapsis
This work considers methods for imposing sparsity in Bayesian regression with applications in nonlinear system identification. We first review automatic relevance determination (AR…
Smoothing and parameter estimation by soft-adherence to governing equations
Samuel Rudy, Steven Brunton, J. Nathan Kutz
The analysis of high-dimensional dynamical systems generally requires the integration of simulation data with experimental measurements. Experimental data often has substantial amo…
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
Samuel H. Rudy, J. Nathan Kutz, Steven L. Brunton
A critical challenge in the data-driven modeling of dynamical systems is producing methods robust to measurement error, particularly when data is limited. Many leading methods eith…
Data-driven identification of parametric partial differential equations
Samuel Rudy, Alessandro Alla, Steven L. Brunton +1
In this work we present a data-driven method for the discovery of parametric partial differential equations (PDEs), thus allowing one to disambiguate between the underlying evoluti…
Data-driven discovery of partial differential equations
Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor +1
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The…