activity
20242026
most citedCapabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra

20 citations · 23 across the 9 of their papers we have counts for

collaborators

11 papers

eess.SY2026

Lipschitz Bounds for Neural Networks Using a Two-Copy Linear Fractional Transformation

Felix Biertümpfel, Bin Hu, Peter Seiler

This paper proposes a new method to bound the global Lipschitz constant of a neural network (NN). Our formulation uses a two-copy linear fractional transformation (LFT) representat…

math.OC2026

Scalable Incremental Robustness Analysis of Neural Network Feedback Systems

Zichen Wang, Peter Seiler, Geir Dullerud +1

Semidefinite programming (SDP) certificates for feedback systems containing deep neural networks (NNs) typically scale with the total number of neurons, whereas small-gain tests ar…

eess.SY2026

Data-Driven Stability and Performance Analysis of Lurye Systems

Sahel Vahedi Noori, Peter Seiler

This paper develops data-driven conditions for certifying internal stability and induced- performance of discrete-time Lurye systems. The Lurye system is an interconnection…

eess.SY2026

Integral Quadratic Constraints for Repeated ReLU

Sahel Vahedi Noori, Bin Hu, Geir Dullerud +1

This paper presents a new dynamic integral quadratic constraint (IQC) for the repeated Rectified Linear Unit (ReLU). These dynamic IQCs can be used to analyze stability and induced…

eess.SY2025

An Exact, Finite Dimensional Representation for Full-Block, Circle Criterion Multipliers

Felix Biertümpfel, Bin Hu, Geir Dullerud +1

This paper provides the first finite-dimensional characterization for the complete set of full-block, circle criterion multipliers. We consider the interconnection of a discrete-ti…

eess.SY2025

Discrete-Time Stability Analysis of ReLU Feedback Systems via Integral Quadratic Constraints

Sahel Vahedi Noori, Bin Hu, Geir Dullerud +1

This paper analyzes internal stability of a discrete-time feedback system with a ReLU nonlinearity. This feedback system is motivated by recurrent neural networks. We first review…

cs.CE2025

Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs

Xingang Guo, Yaxin Li, Xiangyi Kong +62

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. Ho…

eess.SY2024★ 3 cited

ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise

Xingang Guo, Darioush Keivan, Usman Syed +5

Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors including aerospace, automotive systems, power grids, and robo…

cs.LG2024

A Complete Set of Quadratic Constraints for Repeated ReLU and Generalizations

Sahel Vahedi Noori, Bin Hu, Geir Dullerud +1

This paper derives a complete set of quadratic constraints (QCs) for the repeated ReLU. The complete set of QCs is described by a collection of matrix copositivity conditions. We a…

eess.SY2024

Stability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networks

Sahel Vahedi Noori, Bin Hu, Geir Dullerud +1

This paper presents sufficient conditions for the stability and -gain performance of recurrent neural networks (RNNs) with ReLU activation functions. These conditions are d…

math.OC2024★ 20 cited

Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra

Darioush Kevian, Usman Syed, Xingang Guo +5

In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra in solving undergraduate-level contro…