21 citations · 21 across the 7 of their papers we have counts for
7 papers
The SiMPL Method for Multi-Material Topology Optimization
Peter Gangl, Brendan Keith, Dohyun Kim +2
We introduce an efficient and scalable method for density-based multi-material topology optimization, integrating classical mirror descent techniques with point-wise polytopal desi…
Proximal Discontinuous Galerkin Methods for Variational Inequalities
Alexandre Ern, Brendan Keith, Dohyun Kim +2
We introduce a family of proximal discontinuous Galerkin methods for variational inequalities, focusing on the obstacle problem as a didactic example. Each member of this family is…
The proximal Galerkin method for non-symmetric variational inequalities
Guosheng Fu, Brendan Keith, Dohyun Kim +2
We introduce the proximal Galerkin (PG) method for non-symmetric variational inequalities. The proposed approach is asymptotically mesh-independent and yields constraint-preserving…
A Simple Introduction to the SiMPL Method for Density-Based Topology Optimization
Dohyun Kim, Boyan Stefanov Lazarov, Thomas M. Surowiec +1
We introduce a novel method for solving density-based topology optimization problems: Sigmoidal Mirror descent with a Projected Latent variable (SiMPL). The SiMPL method (pronounce…
Analysis of the SiMPL method for density-based topology optimization
Brendan Keith, Dohyun Kim, Boyan S. Lazarov +1
We present a rigorous convergence analysis of a new method for density-based topology optimization that provides point-wise bound preserving design updates and faster convergence t…
High-performance finite elements with MFEM
Julian Andrej, Nabil Atallah, Jan-Phillip Bäcker +15
The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and i…
DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws
Tarik Dzanic, Ketan Mittal, Dohyun Kim +4
We introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost a…