Seminar Details
2025-02-04 (14h) : Newcomers seminars (PhDs)
At Euler building (room a.002)
Organized by Mathematical Engineering
Section 1: Large Language Models for Safety-Critical Control
Speaker :
Amir Bayat (PhD UCLouvain/INMA)
Abstract :
Large Language Models (LLMs) have demonstrated remarkable capabilities in recent years. Their proficiency in tasks such as question answering, text summarization, and code generation has revolutionized various fields, including education, healthcare, and more. LLMs are user-friendly, offering intuitive interfaces that make interaction seamless. However, despite these strengths, they fall short in engineering applications like robotic task planning and execution. In their current state, LLMs are neither reliable nor safe for performing such actions.
On the other hand, symbolic control, also known as abstraction-based control, is a powerful method for managing complex cyber-physical systems. This approach involves designing a controller for an abstracted version of the system and then refining it to control the original system. While effective, this method requires formal language specifications, which demand significant training and expertise to create.
Our long-term goal is to integrate the strengths of both LLMs and symbolic control for cyber-physical systems. By leveraging the user-friendly interaction capabilities of LLMs alongside the safety and reliability of symbolic control, we aim to develop systems that ensure both usability and robustness
Section 2: Data-driven Event-triggered Control for Discrete-time LTI Systems
Speaker :
Vijayanand Digge (PhD UCLouvain/INMA)
Abstract :
Inspired by recent work on data-driven control, this work presents data-driven event-triggered control strategies for discrete-time linear time-invariant (LTI) systems. The results presented do not require explicit identification of the system parameters and are based on the input and state data collected from the system during an open-loop experiment. The design of event-triggered control consists of two stages: finding a state feedback controller that exponentially stabilizes the system and designing an event-triggered policy that determines the instances at which the control law needs to be updated. The proposed designs in both stages involve solving semi-definite programs with data-dependent linear matrix inequalities (LMIs) as constraints. For the event-triggered implementation, we employ a relative thresholding mechanism, and the range of the thresholding parameter is derived using S-procedure. Further, conditions on the thresholding parameter are derived that ensure both pre-specified exponential convergence and non-trivial event-triggering.
Section 3: Impact of Fibre Assignments on Fourier Space Galaxy Clustering Statistics
Speaker :
Jana Jovcheva (PhD UCLouvain/INMA)
Abstract :
Due to instrumental limitations, spectroscopic redshift surveys cannot measure the redshifts for all targets. The physical size of the spectroscopic fibres sets a limit on the angular separation between two galaxies at which their redshifts can be measured simultaneously, known as the fibre collision scale. Fibre allocation algorithms attempt to maximise the number of observed targets and minimise the effects of collisions, but it remains impossible to efficiently measure the redshift of every target. The resulting incompleteness in the data biases galaxy clustering statistics such as the power spectrum and bispectrum, which can prevent robust inference of cosmological parameters. I quantify the impact of fibre assignments on the power spectrum and bispectrum for the Dark Energy Spectroscopic Instrument (DESI) first generation mock catalogues, assess the effectiveness of a simple nearest neighbour correction at recovering the true statistics, and consider two alternative schemes to improve the accuracy of clustering measurements. This analysis provides insights for mitigating fibre collision effects and enhancing the cosmological utility of DESI data and future surveys.
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