6 citations · 6 across the 2 of their papers we have counts for
5 papers
Evaluating LLMs as Interpretable Controllers for Dynamical Systems
Aleksander Ãstensen, Alberto Mino Calero, Anastasios M. Lekkas +1
Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems remains largely unexplored. This…
The Evolution of Digital Twins from Reactive to Agentic Systems
Omer San, Adil Rasheed, Eda Bozdemir +1
Digital twins are evolving into self-learning, autonomous systems that link models, data, and human interaction. Realizing their full potential depends on interoperability, standar…
Large Language Models for Control
Adil Rasheed, Oscar Ravik, Omer San
This paper investigates using large language models (LLMs) to generate control actions directly, without requiring control-engineering expertise or hand-tuned algorithms. We implem…
Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twins
Adil Rasheed, Oscar Ravik, Omer San
This work investigates the use of digital twins for dynamical system modeling and control, integrating physics-based, data-driven, and hybrid approaches with both traditional and A…
Localized PCA-Net Neural Operators for Scalable Solution Reconstruction of Elliptic PDEs
Mrigank Dhingra, Romit Maulik, Adil Rasheed +1
Neural operator learning has emerged as a powerful approach for solving partial differential equations (PDEs) in a data-driven manner. However, applying principal component analysi…