21 citations · 30 across the 6 of their papers we have counts for
6 papers
Exponential Adaptive Smoothing and Importance Sampling for Optimization of the Conditional Value-at-Risk
Will Asness, Brendan Keith, Boyan Lazarov +2
We present a novel method for solving conditional value-at-risk (CVaR) optimization problems based on the dual representation of CVaR, which is defined as the worst-case expectatio…
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…
Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization
Tobias Duswald, Brendan Keith, Boyan Lazarov +2
This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on applications in computational m…
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…
An Adaptive Sampling Augmented Lagrangian Method for Stochastic Optimization with Deterministic Constraints
Raghu Bollapragada, Cem Karamanli, Brendan Keith +3
The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective funct…