activity
20172026
most citedTraining neural networks under physical constraints using a stochastic augmented Lagrangian approach

13 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DC2026

A Portable and Versatile Limited-Memory BFGS Implementation in PETSc/TAO

Hansol Suh, Tobin Isaac, Alp Dener +3

The limited-memory BFGS (L-BFGS) Hessian update scheme is the critical kernel in many quasi-Newton optimization algorithms. The most common approach to implementing L-BFGS uses $2m…

math.NA2022★ 2 cited

Multirate Partitioned Runge-Kutta Methods for Coupled Navier-Stokes Equations

Shinhoo Kang, Alp Dener, Aidan Hamilton +3

Earth system models are complex integrated models of atmosphere, ocean, sea ice, and land surface. Coupling the components can be a significant challenge due to the difference in p…

cs.MS2020★ 6 cited

Toward Performance-Portable PETSc for GPU-based Exascale Systems

Richard Tran Mills, Mark F. Adams, Satish Balay +11

The Portable Extensible Toolkit for Scientific computation (PETSc) library delivers scalable solvers for nonlinear time-dependent differential and algebraic equations and for numer…

physics.comp-ph2020★ 13 cited

Training neural networks under physical constraints using a stochastic augmented Lagrangian approach

Alp Dener, Marco Andres Miller, Randy Michael Churchill +2

We investigate the physics-constrained training of an encoder-decoder neural network for approximating the Fokker-Planck-Landau collision operator in the 5-dimensional kinetic fusi…

physics.plasm-ph2020★ 1 cited

Encoder-decoder neural network for solving the nonlinear Fokker-Planck-Landau collision operator in XGC

M. A. Miller, R. M. Churchill, A. Dener +3

An encoder-decoder neural network has been used to examine the possibility for acceleration of a partial integro-differential equation, the Fokker-Planck-Landau collision operator.…

math.OC2017

Error-tolerant Multisecant Method for Nonlinearly Constrained Optimization

Jason E. Hicken, Pengfei Meng, Alp Dener

We present a derivative-based algorithm for nonlinearly constrained optimization problems that is tolerant of inaccuracies in the data. The algorithm solves a semi-smooth set of no…