8 citations · 16 across the 13 of their papers we have counts for
4 papers · 1 filter
Learning the Evolution of Correlated Stochastic Power System Dynamics
Tyler E. Maltba, Vishwas Rao, Daniel Adrian Maldonado
A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear p…
Randomized Block Adaptive Linear System Solvers
Vivak Patel, Mohammad Jahangoshahi, Daniel Adrian Maldonado
Randomized linear solvers randomly compress and solve a linear system with compelling theoretical convergence rates and computational complexities. However, such solvers suffer a s…
Condensed interior-point methods: porting reduced-space approaches on GPU hardware
François Pacaud, Sungho Shin, Michel Schanen +2
The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the…
Batched Second-Order Adjoint Sensitivity for Reduced Space Methods
François Pacaud, Michel Schanen, Daniel Adrian Maldonado +4
This paper presents an efficient method for extracting the second-order sensitivities from a system of implicit nonlinear equations on upcoming graphical processing units (GPU) dom…