All Years Seminars
[INMA] 2024-04-30 (14h) : Optimization of bridge projects for rivers subject to high geomorphic risk
At Euler building (room A.002)
Speaker :
Hervé Capart (National Taiwan University)
Abstract :
In montane valleys, bridges may be exposed to significant risks of failure due to flood-induced river bed changes and debris fan aggradation. To mitigate these risks in a cost-effective way, we are experimenting with a methodology based on stochastic dynamic programming. An expected lifecycle cost is defined, taking into account the construction cost of the first bridge as well as the time-discounted costs of future bridge reconstructions in case of failure. To determine the timing of the next failure, a Markovian stochastic model of river and fan evolution is used. The objective function can then be recast as a recurrence, and discretized to determine optimal design policies and routes. The approach will be illustrated by examples from the Laonong River valley, southwestern Taiwan.
[INMA] 2024-04-23 (14h) : Markov chains, transport theory and statistical physics
At Maxwell building (Shannon room)
Speaker :
Delvenne, Jean-Charles
Abstract :
We look at the following motivating problem: how to move an electronic memory from a 'zero' state to a 'one' state, at minimal energy cost? Mathematically, this amounts to design a Markov chain that drives a certain probability measure (encoding a 'zero') towards another one (a 'one') through an 'optimal' path --- an avatar of Gaspard Monge's so-called 'earth mover problem', at the core of transport theory. We explore various recent results and conjectures around this theme at the interface of statistical physics and Markov chain theory. We support these by illustrations on realistic simulations on electronic memories.
[INMA] 2024-04-16 (14h) : Safety in Systems and Control: From Theory to Implementation
At Euler building (room A.002)
Speaker :
Guillaume Berger (UCLouvain)
Abstract :
Modern control systems are increasingly deployed in safety-critical applications, such as autonomous vehicles, medical devices, energy grids, etc.
Therefore, verifying and guaranteeing the safety of these systems is of paramount importance.
Nevertheless, it remains a big challenge where NP-hardness and undecidability are the rule rather than the exception.
We review the state-of-the-art theoretical tools for the formal verification and correct-by-design control of dynamical systems such as reachability analysis and barrier functions.
We also discuss the practical implementation of these tools relying on recent advances in computational geometry and optimization.
Finally, we present our contribution to the field by proposing a new approach for safety verification based on multiple barrier functions, called vector barrier functions.
We present the theoretical foundations of this approach as well as the practical implementation leveraging ideas from reachability analysis and convex optimization.
We conclude with some examples showing the efficiency of the approach on benchmark systems
[INMA] 2024-04-02 (14h) : Trends and opportunities in advanced industrial process control
At Euler building (room A.002)
Speaker :
Ian K. Craig (University of Pretoria)
Abstract :
This presentation will look at some trends in industrial process control and opportunities that arise from these. Trends that will be discussed include how the establishment of advanced process controllers are changing, how engineers are increasingly automating tasks related to the maintenance of advanced process controllers, and how operations are transitioning from local to remote operation and control. Some of the driving factors for these trends will be discussed, as well as the challenges and opportunities that arise from them. Examples will be given to illustrate the concepts
[INMA] 2024-03-26 (14h) : Safety of stochastic systems: from stochastic barrier functions to uncertain abstractions
At Euler building (room A.002)
Speaker :
Luca Laurenti (TU Delft)
Abstract :
Providing safety guarantees for stochastic dynamical systems has become a central problem in many fields, including control theory, machine learning, and robotics. In this talk I will present our recent work on providing safety guarantees for non-linear stochastic dynamical systems, including dynamical systems with neural networks in the loop. I will focus on two different approaches to quantify safety for stochastic systems: Stochastic Barrier Functions (SBFs) and abstractions to uncertain Markov models. While SBFs are analogous to Lyapunov functions to prove (probabilistic) set invariance, abstraction-based approaches approximate the stochastic system into a finite model for the computation of safety probability bounds. I will illustrate pros and cons both methods. I will then conclude the talk illustrating how recent results from optimal transport and stochastic approximation could be employed to complement both methods to finally provide scalable safety guarantees for non-linear uncertain systems.
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