Monday 3rd February 2025 – LJLL : Maha Dahoud (ENSTA Paris)

Title: A class of parabolic fractional reaction-diffusion systems with control of total mass: Theory and numerics Abstract: In this talk based on [1, 2], we present some new results about global-in-time existence of strong solutions to a class of fractional parabolic reaction–diffusion systems posed in a bounded open subset of…

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Monday 20th January 2025 – Visio : Tabea Tscherpel (TU Darmstadt)

Title: Energy consistent time discretisation of port-Hamiltonian systems Abstract Various ordinary and partial differential equations arising from physics can be written as port-Hamiltonian systems. Their Hamiltonian function represents an energy that is conserved or dissipated along solutions. Numerical schemes are energy consistent, if the Hamiltonian is preserved or dissipated also…

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Monday 7th February 2022- Visio
Federica Caforio Universität Graz

Development and personalisation of coupled 3d-1d models of cardiovascular function A key factor governing the mechanical performance of the heart is the bidirectional coupling with the cardiovascular system where alterations in the arterial system modulate the pulsatile load imposed on the heart. Current image-based computational models of cardiac electro-mechanics (EM)…

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Monday 20th January 2020- INRIA
Mathieu Mézache (Sorbonne Université) .

Modeling oscillating polymerization/depolymerization processes The process of protein aggregation and fragmentation is intimately linked to the development of a broad class of neurodegenerative diseases or amyloid disease. More particularly, oscillatory kinetic phenomena are identified in experiments on Prion diseases. As a first step, the analysis of experimental data leads us…

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Monday 4th November 2019- LJLL
Anthony Nouy (École Centrale de Nantes).

Title : Approximation with tree tensor networks Abstract: Many problems in computational science require the approximation of high-dimensional functions. Examples of such problems can be found in physics, stochastic analysis or statistical learning. Other examples include parametric or uncertainty analyses for parameter-dependent models. The approximation of a high-dimensional function requires…

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