SUPR
Learning based algorithm development for stochastic systems
Dnr:

NAISS 2025/22-1116

Type:

NAISS Small Compute

Principal Investigator:

Arunava Naha

Affiliation:

Linköpings universitet

Start Date:

2025-08-22

End Date:

2026-09-01

Primary Classification:

20202: Control Engineering

Webpage:

Allocation

Abstract

I am currently working on two different problems, both of which require CPU-intensive computations, with additional GPU support being a valuable resource. The first problem involves controlling a closed-loop dynamical system with safety or risk constraints using actor-critic based reinforcement learning. The second problem focuses on developing a differentially private federated Gaussian process regression algorithm. To support our study, we need to conduct extensive simulation studies for both problems. While the computations are primarily CPU-intensive, we will utilize shallow neural networks in our models, which may benefit from additional GPU resources. Additionally, we will need to leverage multiple CPU cores in parallel.