14 citations · 16 across the 9 of their papers we have counts for
9 papers
GPU-Accelerated DNS of Compressible Turbulent Flows
Youngdae Kim, Debojyoti Ghosh, Emil M. Constantinescu +1
This paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar, for compressible flow simulations on emerging exascal…
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning
Minseok Ryu, Youngdae Kim, Kibaek Kim +1
Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in…
A Globally Convergent Distributed Jacobi Scheme for Block-Structured Nonconvex Constrained Optimization Problems
Anirudh Subramanyam, Youngdae Kim, Michel Schanen +2
Motivated by the increasing availability of high-performance parallel computing, we design a distributed parallel algorithm for linearly-coupled block-structured nonconvex constrai…
A mixed complementarity problem approach for steady-state voltage and frequency stability analysis
Youngdae Kim, Kibaek Kim
We present a mixed complementarity problem (MCP) approach for a steady-state stability analysis of voltage and frequency of electrical grids. We perform a theoretical analysis prov…
Accelerated Computation and Tracking of AC Optimal Power Flow Solutions using GPUs
Youngdae Kim, Kibaek Kim
We present a scalable solution method based on an alternating direction method of multipliers and graphics processing units (GPUs) for rapidly computing and tracking a solution of…
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow
Sihan Zeng, Alyssa Kody, Youngdae Kim +2
With the increasing penetration of distributed energy resources, distributed optimization algorithms have attracted significant attention for power systems applications due to thei…