Thomas Mongaillard

Thomas Mongaillard

1st year PhD student

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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Posts

Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Optimal Carbon Pricing Based on Dynamical Climate Model

Published in Book of abstracts PGMO DAYS 2023, 2023

In this work, we adapt an optimal pricing framework [1] to the case of carbon pricing by including a climate model. We consider a set of countries N willing to maximize their own utility function modeled by \[\forall n \in N, u_n(e_n,P)= b_n(en)−P e_n\] with en the CO2 emission level of country \(n\), \(P\) the price of carbon and \(b_n\) is the benefit function of country \(n\). These benefit functions are assumed to be sigmoidal and represent the evolution of the gross domestic product (GDP) of the country depending on its CO2 emissions…

Recommended citation: T. Mongaillard, S. Lasaulce, V. Varma, A. Couthures, and E. Sabir, “Optimal Carbon Pricing Based on Dynamical Climate Model,” Book of abstracts PGMO DAYS 2023, p. 82, 2023. https://fondation-hadamard.fr/media/filer_public/27/5e/275ef5e6-b213-4b91-b472-0572d807a64e/pgmo-2023-booklet.pdf#page=98

Analysis of a continuous opinion and discrete action dynamics model coupled with an external observation dynamics

Published in ECC 2024, 2024

We consider a set of consumers in a city or town (who thus generate pollution) whose opinion is governed by a continuous opinion and discrete action (CODA) dynamics model. This dynamics is coupled with an observation signal dynamics, which defines the information the consumers have access to regarding the common pollution. We show that the external observation signal has a significant impact on the asymptotic behavior of the CODA model. When the coupling is strong, it induces either a chaotic behavior or convergence towards a limit cycle. When the coupling is weak, a more classical behavior characterized by local agreements in polarized clusters is observed. In both cases, conditions under which clusters of consumers don’t change their actions are provided.Numerical examples are provided to illustrate the derived analytical results.

Recommended citation: A. Couthures, T. Mongaillard, V. S. Varma, S. Lasaulce, and I.-C. Morarescu, “Analysis of a continuous opinion and discrete action dynamics model coupled with an external observation dynamics,” Mar. 18, 2024, arXiv: arXiv:2403.09473. doi: 10.48550/arXiv.2403.09473. https://arxiv.org/abs/2403.09473

Large Language Models for Power Scheduling: A User-Centric Approach

Published in arXiv, 2024

While traditional optimization and scheduling schemes are designed to meet fixed, predefined system requirements, future systems are moving toward user-driven approaches and personalized services, aiming to achieve high quality-of-experience (QoE) and flexibility. This challenge is particularly pronounced in wireless and digitalized energy networks, where users' requirements have largely not been taken into consideration due to the lack of a common language between users and machines. The emergence of powerful large language models (LLMs) marks a radical departure from traditional system-centric methods into more advanced user-centric approaches by providing a natural communication interface between users and devices. In this paper, for the first time, we introduce a novel architecture for resource scheduling problems by constructing three LLM agents to convert an arbitrary user's voice request (VRQ) into a resource allocation vector. Specifically, we design an LLM intent recognition agent to translate the request into an optimization problem (OP), an LLM OP parameter identification agent, and an LLM OP solving agent. To evaluate system performance, we construct a database of typical VRQs in the context of electric vehicle (EV) charging. As a proof of concept, we primarily use Llama 3 8B. Through testing with different prompt engineering scenarios, the obtained results demonstrate the efficiency of the proposed architecture. The conducted performance analysis allows key insights to be extracted. For instance, having a larger set of candidate OPs to model the real-world problem might degrade the final performance because of a higher recognition/OP classification noise level. All results and codes are open source.

Recommended citation: T. Mongaillard et al., “Large Language Models for Power Scheduling: A User-Centric Approach,” Jun. 29, 2024, arXiv: arXiv:2407.00476. Accessed: Jul. 02, 2024. [Online]. Available: http://arxiv.org/abs/2407.00476 https://arxiv.org/abs/2407.00476

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