1 citations · 1 across the 3 of their papers we have counts for
5 papers
Dimension-independent eigenvalue approximation for sparse matrices
Natalia Hajlasz, Christopher Musco, Jonathan Weare +1
Many applications in science and engineering require solving sparse Hermitian eigenvalue problems that are so large that even algorithms whose runtime scales linearly with the matr…
Explaining El Niño Forecasts with the Average Gradient Outer Product
Yuan Hui, Dorian S. Abbot, Robert J. Webber
An important and unresolved problem in the physical sciences is explaining the predictions made by neural networks. Several explainable artificial intelligence (XAI) methods have b…
Improved energies and wave function accuracy with Weighted Variational Monte Carlo
Huan Zhang, Robert J. Webber, Michael Lindsey +2
Neural network parametrizations have increasingly been used to represent the ground and excited states in variational Monte Carlo (VMC) with promising results. However, traditional…
AI can identify Solar System instability billions of years in advance
Dorian S. Abbot, J. D. Laurence-Chasen, Robert J. Webber +2
Rare event schemes require an approximation of the probability of the rare event as a function of system state. Finding an appropriate reaction coordinate is typically the most cha…
Randomly sparsified Richardson iteration: A dimension-independent sparse linear solver
Jonathan Weare, Robert J. Webber
Recently, a class of algorithms combining classical fixed point iterations with repeated random sparsification of approximate solution vectors has been successfully applied to eige…