3 citations · 3 across the 5 of their papers we have counts for
6 papers
Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations
Aditya Makkar, Benjamin Unger, Jeongyeol Kwon +3
Modern multi-agent systems are increasingly deployed at scale over large populations of agents in settings such as ad-auctions, traffic routing, and recommendation systems. The dom…
Nonlinear model reduction for transport-dominated problems
Jan S. Hesthaven, Benjamin Peherstorfer, Benjamin Unger
This article surveys nonlinear model reduction methods that remain effective in regimes where linear reduced-space approximations are intrinsically inefficient, such as transport-d…
SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
Yarden As, Chengrui Qu, Benjamin Unger +6
Deploying reinforcement learning (RL) safely in the real world is challenging, as policies trained in simulators must face the inevitable sim-to-real gap. Robust safe RL techniques…
Prediction error certification for PINNs: Theory, computation, and application to Stokes flow
Birgit Hillebrecht, Benjamin Unger
Rigorous error estimation is a fundamental topic in numerical analysis. With the increasing use of physics-informed neural networks (PINNs) for solving partial differential equatio…
model order reduction for quadratic output systems
Birgit Hillebrecht, Benjamin Unger
Linear time-invariant quadratic output (LTIQO) systems generalize linear time-invariant systems to nonlinear regimes. Problems of this class occur in multiple applications naturall…
Control of port-Hamiltonian differential-algebraic systems and applications
Volker Mehrmann, Benjamin Unger
The modeling framework of port-Hamiltonian descriptor systems and their use in numerical simulation and control are discussed. The structure is ideal for automated network-based mo…