5 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…