88 citations · 257 across the 20 of their papers we have counts for
21 papers
eGAD! double descent is explained by Generalized Aliasing Decomposition
Mark K. Transtrum, Gus L. W. Hart, Tyler J. Jarvis +1
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is under…
Methodological Reconstruction of Historical Landslide Tsunamis Using Bayesian Inference
Raelynn Wonnacott, Dallin Stewart, Jared P Whitehead +1
Indonesia is one of the world's most densely populated regions and lies among the epicenters of Earth's greatest natural hazards. Effectively reducing the disaster potential of the…
A tale of two faults: Statistical reconstruction of the 1820 Flores Sea earthquake using tsunami observations alone
T. Paskett, J. P. Whitehead, R. A. Harris +4
Using a Bayesian approach we compare anecdotal tsunami runup observations from the 29 December 1820 Flores Sea earthquake with close to 200,000 tsunami simulations to determine the…
Identifying the body force from partial observations of a 2D incompressible velocity field
Aseel Farhat, Adam Larios, Vincent R. Martinez +1
Using limited observations of the velocity field of the two-dimensional Navier-Stokes equations, we successfully reconstruct the steady body force that drives the flow. The number…
Dynamically learning the parameters of a chaotic system using partial observations
Elizabeth Carlson, Joshua Hudson, Adam Larios +3
Motivated by recent progress in data assimilation, we develop an algorithm to dynamically learn the parameters of a chaotic system from partial observations. Under reasonable assum…
Embracing Uncertainty in "Small Data" Problems: Estimating Earthquakes from Historical Anecdotes
Nathan E. Glatt-Holtz, Ronald A. Harris, Andrew J. Holbrook +4
Seismic risk estimates will be vastly improved with an increased understanding of historical (and pre-historical) seismic events. However the only existing data for these events is…