Seminar in PDE and Applications
The idea of this seminar is to bring together local and international experts in the field of partial differential equations (PDE) and related fields. In the talks we want to cover various aspects of PDE and their applications, including modeling, mathematical analysis and numerics. Our goal is to increase the visibility of work that is being done in the field of PDE across the different groups at DIAM and to serve as a meeting platform within the department.
The seminar takes place on Thursdays at 4pm.
If you are interested in talks about topics in Applied Analysis, check out the seminar page.
Yves van Gennip (MP)
Anna Geyer (MP)
Manuel Gnann (MP)
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14 december 2023 16:00 t/m 17:00
[PDE & Applications seminar] Clarice Poon: Sparsistency for inverse optimal transportOptimal Transport is a useful metric to compare probability distributions and to compute a pairing given a ground cost. Its entropic regularization variant (eOT) is crucial to have fast algorithms and reflect fuzzy/noisy matchings. This work focuses on Inverse Optimal Transport (iOT), the problem of inferring the ground cost from samples drawn from a coupling that solves an eOT problem. It is a relevant problem that can be used to infer unobserved/missing links, and to obtain meaningful information about the structure of the ground cost yielding the pairing. On one side, iOT benefits from convexity, but on the other side, being ill-posed, it requires regularization to handle the sampling noise. This work presents an in-depth theoretical study of the $\ell_1$ regularization to model for instance Euclidean costs with sparse interactions between features. Specifically, we derive a sufficient condition for the robust recovery of the sparsity of the ground cost that can be seen as a far-reaching generalization of the Lasso’s celebrated ``Irrepresentability Condition’’. To provide additional insight into this condition, we work out in detail the Gaussian case. We show that as the entropic penalty varies, the iOT problem interpolates between a graphical Lasso and a classical Lasso, thereby establishing a connection between iOT and graph estimation, an important problem in ML. This is joint work with Francisco Andrade and Gabriel Peyré.
11 januari 2024 16:00 t/m 17:00
[PDE & Applications seminar] Oliver Tse: Generalized gradient structures for interacting population dynamicsIn this talk, we discuss a class of non-local evolution equations arising from reversible interacting birth-and-death processes that can be given a generalized gradient structure, and further motivate the structure via the large-population limit of measure-valued processes in population dynamics.
25 januari 2024 16:00 t/m 17:00
[PDE & Applications seminar] Daniele Avitabile: Uncertainty Quantification for Neurobiological NetworksThis talk presents a framework for forward uncertainty quantification problems in spatially-extended neurobiological networks. We will consider networks in which the cortex is represented as a continuum domain, and local neuronal activity evolves according to an integro-differential equation, collecting inputs nonlocally, from the whole cortex. These models are sometimes referred to as neural field equations.
Large-scale brain simulations of such models are currently performed heuristically, and the numerical analysis of these problems is largely unexplored. In the first part of the talk I will summarise recent developments for the rigorous numerical analysis of projection schemes  for deterministic neural fields, which sets the foundation for developing Finite-Element and Spectral schemes for large-scale problems.
The second part of the talk will discuss the case of networks in the presence of uncertainties modelled with random data, in particular: random synaptic connections, external stimuli, neuronal firing rates, and initial conditions. Such problems give rise to random solutions, whose mean, variance, or other quantities of interest have to be estimated using numerical simulations. This so-called forward uncertainty quantification problem is challenging because it couples spatially nonlocal, nonlinear problems to large-dimensional random data.
I will present a family of schemes that couple a spatial projector for the spatial discretisation, to stochastic collocation for the random data. We will analyse the time- dependent problem with random data and the schemes from a functional analytic viewpoint, and show that the proposed methods can achieve spectral accuracy, provided the random data is sufficiently regular. We will showcase the schemes using several examples.
Acknowledgements This talk presents joint work with Francesca Cavallini (VU Amsterdam), Svetlana Dubinkina (VU Amsterdam), and Gabriel Lord (Radboud University).
 Avitabile D. (2023). Projection Methods for Neural Field Equations. https://www.danieleavitabile.com/projection-methods-for-neural-field-equations/
07 maart 2024 16:00 t/m 17:00
[PDE&A] Fred Wubs: Bifurcation Analysis of Fluid FlowsA better understanding of the mechanisms leading a fluid system to exhibit turbulent behavior is one of the grand challenges of the physical and mathematical sciences. Over the last few decades, numerical bifurcation methods have been extended and applied to a number of flow problems to identify critical conditions for fluid instabilities to occur. Together with Henk Dijkstra from IMAU, I taught a course in MasterMath which led to a book that provides a state-of-the-art account of these numerical methods. These methods also have a broad applicability in industrial, environmental and astrophysical flows.
In the presentation, I will discuss numerical methods for bifurcation analysis and show their application to some fluid flow problems.
More information on the book and accompanying software etc. can be found on