21/05/2024 | Francesco Cattafi | An overview on Lie pseudgroups and geometric structures

ABSTRACT:

The space of (local) symmetries of a given geometric structure has the natural structure of a Lie (pseudo)group. Conversely, geometric structures admitting a local model can be described via the pseudogroup of symmetries of such local model.
The main goal of this talk is to provide several examples and give an intuitive understanding of the slogan above, which can be made precise at various levels of generality (depending on the definition of “geometric structure”) and using different tools/methods.
Moreover, I will sketch a new framework, which include previous formalisms (e.g. G-structures or Cartan geometries) and allows us to prove integrability theorems. In particular, I will provide intuition on the relevant objects which make this approach work, namely Lie groupoids endowed with a multiplicative “PDE-structure” and their principal actions. Poisson geometry will give us the guiding principles to understand those objects, which are directly inspired from, respectively, symplectic groupoids and principal Hamiltonian bundles. This is based on joint work with Luca Accornero, Marius Crainic and María Amelia Salazar.

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16/05/2024 | Jagna Wisniewska | Rabinowitz Floer homology for non-compact hypersurfaces

ABSTRACT:

Modern symplectic geometry started as a mathematical formulation of classical mechanics. Examples of Hamiltonian dynamical systems arise in celestial mechanics or electromagnetism. However, even though the equations are known, explicit computations of the trajectories might often be too complex. Rabinowitz Floer homology is an algebraic invariant, which relates the existence of periodic solutions of Hamiltons equations on a prescribed energy hypersurface to its geometrical and topological properties. In my talk I will show how to extend this construction to include a class of non-compact hypersurfaces.

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18/04/2024 | Alfonso Garmendia (Centre de Recerca Matemàtica – CRM) | Gauge theories in groupoids

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04/04/2024 | José Lamas Rodriguez (Universitat Politècnica de Catalunya – UPC) | Collision leading to chaos in the restricted planar three-body problem

ABSTRACT:

We consider a very important problem in the field of celestial mechanics, called the restricted planar circular three-body problem. In this setup, we are examining how a small, massless object moves under the gravitational influence of two larger bodies, which are orbiting each other in circular trajectories around a common center.

When we look at this problem in the right mathematical framework, we find that it is governed by what are called Hamiltonian dynamics, which essentially means that energy is conserved. This energy is often referred to as the Jacobi constant.

For our interest, we will focus on the so called ejection-collision orbits. These are trajectories where the small object starts off near one of the bigger bodies, gets flung out into space for a while, and the comes crashing back into it.

We are going to explore how we can create these orbits, especially when the masses involved are really lopsided. We introduce some mathematical concepts, like the stable and unstable manifolds, which help us understand where these orbits come together. Essentially, we are going to show that for certain conditions, there are points where the trajectories of these orbits intersect in a particular way, leading to different kind of behaviour.

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14/03/2024 | Albert Solà Vilalta (Universitat Politècnica de Catalunya – UPC) | Optimal Participation of Energy Communities in Electricity Markets under Uncertainty: A Multi-Stage Stochastic Programming Approach

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29/02/24 | Marina Garrote-López (Max Planck Institute for Mathematics in the Sciences) | Identifiability of level-1 species networks from gene tree quartets

ABSTRACT:

Understanding evolutionary relationships, particularly in the context of hybridization and horizontal gene transfer, requires the inference of phylogenetic networks rather than traditional trees. While standard phylogenetic methods can infer gene trees from genetic data, these trees only indirectly reflect the species network topology due to horizontal inheritance and incomplete lineage sorting. Previous research has shown that certain network topologies and numerical parameters can be identified, but gaps remain in understanding the full topology of level-1 phylogenetic networks under the Network Multispecies Coalescent model.

In this talk, we will aim to fill these gaps and address both the identifiability of the full topology of the network and the numerical parameters by investigating the ideals defined by quartet concordance factors for topological semi-directed networks.

Joint work with Elizabeth S. Allman, Hector Baños and John A. Rhodes.

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01/02/24 | Irene Spelta (Centre de Recerca Matemàtica) | Moduli spaces in Algebraic Geometry

ABSTRACT:

In this talk, we will address the problem of classifying objects up to a certain equivalence relation. It turns out that, sometimes, the quotient space, namely the set S of equivalence classes, can be provided with a “natural” geometric structure. If this is the case, S is called a moduli space. We will analyse some examples. In the end, we will focus on the moduli space of smooth projective genus g curves, and we will discuss some open questions.

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18/01/24 | Gloria Cecchini (Centre de Recerca Matemàtica) | Mean-field model of consequential reward-driven decision-making

ABSTRACT:

How is the knowledge of consequence incorporated into decisions between reward-driven visual options? Despite the vast literature on reward driven decision-making (Soltani et al. 2006, Wong Wang 2006, Marcos et al. 2013), the neural computations underlying option complexity and their potential consequence on our daily decision-making processes remain to be well understood and properly formalized.

Towards this end, we introduce a theoretical formalization of decision-making that reproduces an approximation of the dynamics of two neural populations, capable of learning sequences of binary decisions adaptively to maximize cumulative reward. Our model incorporates complexity, visual stimuli discrimination and predicted consequence into its dynamics in a parsimonious fashion. Furthermore, our model also embodies internal oversight mechanisms of consequence, and of learning by reinforcement.

We have tested this model with behavioural results of an ad-hoc experimental task. Human participants were instructed to perform sequences of binary decisions aiming at maximizing cumulative reward throughout the sequence, often in the absence of explicit reward. In brief, at each trial the participants had to select between two presented visual stimuli. The magnitude of the stimuli was conditioned to the participants’ previous decisions (the consequence). Remarkably, the predictions of our model reproduced both the non-trivial inhibitory patterns of decision-making, as well as the sequences of decisions of each individual pattern, regardless of their level of performance throughout the experimental session.

In conclusion, we introduce a novel model of decision-making that incorporates the notion of consequence during the decision-making process and learns optimal strategies.

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14/12/2023 | Filip Ivančić Postdoctoral (Centre de Recerca Matemàtica) | Dynamical system governing ∅ resund trophic chain

ABSTRACT:

30/11/2023 | Simon Syga (TU Dresden) | Emerging tumor heterogeneity predicts variability in treatment response

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02/11/2023 | Kevin Martínez Añón (Universitat Autònoma de Barcelona – Centre de Recerca Matemàtica) | Biological pattern formation beyond Turing

ABSTRACT:
In a seminal paper of 1952, Alan Turing proved that a simple system of two different chemical species that diffuse and antagonistically interact with each other is enough to produce spatially heterogeneous solutions along a domain, which he cleverly proposed as a physicochemical explanation to cell differentiation in embryo development. However, the apparently fine tuning of some parameters, particularly the high difference of diffusivity between species, that theory predicts for the formation of the pattern have raised doubts on the actual feasibility of Turing’s mechanism as a mechanism of pattern formation in real-life systems ever since.

In this talk we present an alternative, brand-new mechanism of biological pattern formation that proves that spatially heterogeneous solutions are also possible in a system with just one diffusible species, thus overcoming many of the drawbacks of Turing’s mechanism. Additionally, we use some toy models to show the wide range of different dynamical features underlying the process of pattern formation in this new scenario.
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19/10/2023 | Marina Vegué (Universitat Politècnica de Catalunya) | Reducing the dynamics of a large interacting system

ABSTRACT:
Dimension reduction is a common strategy to study non-linear dynamical systems composed by a large number of variables. The goal is to find a smaller version of the system whose time evolution is easier to predict while preserving some of the key dynamical features of the original system. Finding such a reduced representation for  complex systems is, however, a difficult task. I will address this problem for dynamics on weighted directed networks, with special emphasis on modular and heterogeneous networks. We propose a two-step dimension-reduction method that takes into account the properties of the adjacency matrix. First, units are partitioned into groups of similar connectivity profiles. Each group is associated to an observable that is a weighted average of the nodes’ activities within the group. Second, we derive a set of equations that must be fulfilled for these observables to properly represent the original system’s behavior, together with a method for approximately solving them. The result is a reduced adjacency matrix and an approximate system of ODEs for the observables’ evolution. I will show that the reduced system can be used to predict some characteristic features of the complete dynamics for different types of connectivity structures, both synthetic and derived from real data.
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05/10/2023 | Gerard Farré Puiggalí (UPC) | On the optimality of effective stability bounds for invariant tori of Hamiltonian Systems

ABSTRACT:

In this talk, I will introduce the concept of effective stability for invariant objects that naturally arise in Hamiltonian Systems, such as invariant tori. We will observe that under specific conditions, invariant tori exhibit a form of “stickiness,” or equivalently, it can be proved that it takes a considerable amount of time for solutions with initial conditions close to these objects to diverge from them. I will then elaborate on how to construct examples to show the optimality of these results.

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21/09/2023 | Cristina Crespo (URJC – CRM) | What can statistics tell us about earthquakes?

ABSTRACT:

In this talk, I will point out the relevance of earthquakes inside the tectonic plates (intraplate), and how thanks to statistical methods, we can shed light on the origin of these poorly understood earthquakes. Throughout my PhD, several statistical methods are applied to clear up the driven mechanism of the seismicity in the intraplate region of NW Iberian Peninsula, and in a small region of Lugo characterized by moderate (5.1 Mw y 4.6 Mw) and continuous seismicity (for more than 25 years). Among other statistical techniques applied, the ETAS model is remarkable, because it is widely used to characterize and identify triggered seismicity. Our results suggested the hypothesis of a mix of tectonic thrust and fluid upward migration as responsible for the seismicity in this region. During my postdoc at the CRM we will apply these models of seismic triggering after larger earthquakes to other examples, such as the 1755 Lisbon earthquake.

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08/06/2023 | Román Moreno (Universitat Politècnica de Catalunya) | Instabilities in Hamiltonian systems: a study of the original Arnold model

ABSTRACT:

The goal of this talk is to introduce the concept of Arnold diffusion, to explain where it lies inside the area of study of differential equations and to present my specific research topic. First we will contextualize the study of Arnold diffusion. For that, we will trace historically the development of the area of Hamiltonian systems highlighting the question of stability and its importance for physical models. Then, we will look at the more abstract mathematical setting of perturbation theory developed to tackle the problem of stability. We will define Arnold diffusion and we will outline the relevant questions researchers are interested in. Finally, I will focus on my study of Arnold’s original problem using modern techniques of analysis of exponentially small phenomena.

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25/05/2023 | Marvin Weidner (Universitat de Barcelona) | About melting ice and pricing American options: Regularity for the obstacle problem

ABSTRACT:

Various phenomena in physics, biology, finance, and other areas can be described mathematically by so-called free boundary problems. Solving the corresponding partial differential equations does not only consist in finding the solution itself, but also in determining an a priori unknown interface. The obstacle problem is a free boundary problem modeling for instance the melting of ice or the pricing of American options.

In the first part of the talk, we will introduce the obstacle problem, explain some of its mathematical features, and study the regularity of the solution and of the free boundary. The second part deals with the nonlocal obstacle problem. This is a related model taking into account long-range interactions. We will discuss a recent result on the regularity for the nonlocal obstacle problem. This talk is based on a joint work with Xavier Ros-Oton and Damià Torres-Latorre.

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11/05/2023 | Mónica Minjares (CRM) | Statistical Analysis of the Madden-Julian Oscillation Events

ABSTRACT:

In this talk I will present an analysis of the statistical features of the Madden-Julian Oscilation (MJO) events. MJO is a weather structure that propagates eastward along the globe and near the equator, it has a strong influence on the precipitation in the tropics but it also affects extratropical regions through teleconnection patterns.

Although several studies have widely analysed the MJO, its activation and evolution are not fully understood.

MJO is of great importance as it provides a major source of tropical and extratropical predictability on subseasonal time scales (two weeks to three months). To monitor MJO we use a Real-time Multivariate (RMM) index which consists of the first and second principal components (RMM1 and RMM2) obtained from the empirical orthogonal functions (EOF) that combine latitudinal averages of outgoing longwave radiation (OLR), and winds at lower and higher atmospheric levels.

In our framework an event takes place when the index amplitude (the modulus of the vector (RMM1,RMM2)) exceeds a certain threshold. With this, we define the observables of an event; these are, the maximum amplitude, duration,  the period in which the amplitude is above the threshold and size, which is the sum of the amplitudes along the duration of an event. We further explore whether the tails of the event distributions are heavy tailed. A distribution is heavy-tailed if the tail (right or left) decays more slowly than those of any exponential distribution. They are of interest because they can be used to model processes which have a tendency to produce extreme outcomes. Of particular interest are extreme MJO events and for this we use extreme-value theory in concrete, the peak-over-threshold framework. We then make a composite analysis of these extreme events considering the phase (geographical location) of initiation of the events to see how they affect different regions away from the equator.

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27/04/2023 | Lucy Auton (Centre de Recerca Matemàtica) | Macroscopic Contaminant Transport & Removal in Soft & Heterogeneous Filters

ABSTRACT:

Two key challenges that if tackled can drastically improve sustainability are removing pollutants from waste products of industrial processes to prevent them contaminating our air or water and decontaminating our existing resources especially in regions of high demand but low availability. Despite the importance of contaminant transport and removal in such filters, the effect of microstructure on flow and transport remains poorly understood and improving our understanding of this remains a major challenge. I consider fluid flow through  a two-dimensional microstructure comprising an array of obstacles of smooth but arbitrary shape, the size and spacing of which can vary along the length of the porous medium, allowing for anisotropy.  To analyse the dispersive effects, I use multiple temporal scales and a nontrivial extension to classical homogenisation theory via the method of multiple scales to rigorously upscale the problem involving cells of varying area. Subsequently, I  perform a drift transformation to account for the three spatial scales intrinsically present in dispersive problems. This results in simple effective continuum equations for macroscale flow and dispersive transport where the effect of the microscale geometry on the macroscopic transport and removal is encoded within these simple macroscale equations via effective parameters such as an effective local anisotropic diffusivity and an effective local adsorption rate.  I determine these effective parameters numerically for a simple example geometry.

This model constitutes the development of the core framework required to consider other crucial problems such as solute transport within soft porous materials for which there does not currently exist a simple macroscale model where the effective diffusivity, dispersivity and removal depend on the microstructure. Further, via this methodology I will derive a bespoke model for fluoride and arsenic removal filters. With this model I will be able to optimise the design of fluoride-removal filters which are being deployed across rural India. The design optimisation will both increase filter lifespan and reduce filter cost, enabling more people to access safe drinking water.

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13/04/23 | Jessica Elisa Massetti (Research fellow at Università Roma Tre) | Recursive phenomena and lifespan of solutions in Hamiltonian PDEs

ABSTRACT:

Many physical phenomena have ondulatory nature: the motion of the sea, the deflection of a beam, electromagnetic waves (light, radio waves)… Many of these are modelled by nonlinear Hamiltonian PDEs and their mathematical description is often extremely complicated, characterized by a non trivial interplay between stable and chaotic behaviours. Of course, the typical behaviour of the solutions is strongly related to the boundary conditions: in this talk I shall deal with (very simple) compact domains. Two fundamental questions arise naturally: the existence of recursive solutions (periodic, quasi-periodic or almost periodic in time) and long time stability of initial data. I shall describe some of these models and discuss the main difficulties in dealing with these questions, through a dynamical system approach. Finally, I shall describe some very recent results in collaboration with R. Feola, concerning the long time behavior of Sobolev norms, via Normal Form and Energy methods.

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30/03/23 | Giulia Binotto (Postdoctoral researcher at UAB) | Can the memory loss help to study the propagation of a disease?

ABSTRACT:

Mathematical models applied to the field of biology and, in particular, epidemiology are currently of great interest due to the pandemic generated by COVID-19. However, their study to describe the spread of a disease has a long history. It dates back to the research on smallpox inoculation carried out by Bernoulli in the 18th century. Since then, both deterministic and stochastic models have been considered and many factors have been taken into account: infectious agents, mode of transmission, latency periods, temporary or partial immunity, quarantine periods, etc.

The aim of this talk is to present a SIR-type stochastic model, which is an extension of the one proposed by Tuckwell and Williams in 2007. It is a discrete-time Markovian model in which the total population remains constant and individuals meet a random number of other individuals at each time step. One of its innovative aspects is the time dependence of this parameter.

An analytic description of the model and its dynamics will be provided.

In order to better comprehend them, a brief introduction to Markov chains will be given and their connection with memory loss will be explained. Finally, with the aid of some simulations, it will be shown how the evolution of a disease is affected by the time dependence of the number of daily encounters.

These results are part of a work carried out with M. Besalú.

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16/03/23 | Blai Vidiella (Postdoctoral researcher at CSIC – Institut de Biologia Evolutiva) | Complex Systems to understand the Biosphere’s Past, Present, and Future

ABSTRACT:

Since the dawn of humanity, scientists have been captivated by the intricate and complex systems found in nature. From the movements of celestial bodies to the behavior of microorganisms, we have sought to understand and explain the phenomena we observe in the natural world. Today, as we face increasing challenges such as climate change and ecosystem degradation, our understanding of these systems has never been more important. In this talk, I will explore the power of mathematical modeling in helping us to gain a deeper understanding of the biological systems that make up the biosphere. Specifically, I will focus the application of mathematical rules to understand evolutionary questions, such as punctuated equilibria; the role of mathematical modeling in studying anthropogenic ecology; and the concept of ‘ghost ecosystems’ resulting from climate change. Through this discussion, I hope to shed light on the exciting possibilities that lie ahead for those working at the intersection of mathematics and biology.

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02/03/2023 | Manuel Molano (CRM) | Investigating how rats make decisions

ABSTRACT:

In this talk I will briefly explain how neuroscientists investigate the strategies used by animals to make decisions and the neural mechanisms underlying such strategies. I will then provide an example in which we have studied the behavior of rats performing a task in which they have to listen to an auditory stimulus and decide between two alternatives basing their decision on the information provided by the stimulus. By tracking the position of the rats using a machine learning tool we were able to characterize how they combine their previous experience with the incoming stimulus information, and show that they flexibly update an initial decision by incorporating new incoming information.

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19/01/23 | Giovanni Dalmasso (CRM) | 4D reconstruction of developmental trajectories using spherical harmonics

ABSTRACT:

Normal organogenesis cannot be recapitulated in vitro for mammalian organs, unlike in species including Drosophila and zebrafish. Available 3D data in the form of ex vivo images only provide discrete snapshots of the development of an organ morphology. Here, we present a computer-based approach to recreate its continuous evolution in time and space from a set of 3D volumetric images. This method is based on the remapping of shape data into the space of the coefficients of a spherical harmonics expansion where a smooth interpolation over time is simpler. This approach had been tested on mouse limb buds and embryonic hearts. A key advantage of this method is that the resulting 4D trajectory can take advantage of all the available data while also being able to interpolate well through time intervals for which there are little or no data. This allows for a quantitative, data-driven 4D description of mouse limb morphogenesis.

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15/12/22 | Alfonso Garmendia (CRM) | Deformation Quantization

ABSTRACT: The goal of quantization is to associate a C*-algebra (a quantum space) to any Poisson manifold (a classical space) in a way that preserves the symmetries. This talk tries to convey the motivation and a summary on deformation quantization with examples.

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01/12/22 | Angelica Torres (CRM) | Algebraic Geometry and line reconstruction in Computer Vision

ABSTRACT:

The 3D image reconstruction problem aims to create a 3D model of a scene or object starting from 2D images. This process is done in four stages: Feature identification in the images, point and line matching, camera estimation and triangulation, and construction of the 3D model. Stages two and three of the process deal mainly with geometric information that can be studied algebraically. This is precisely where Algebraic Geometry comes into play. In this talk I will introduce the 3D image reconstruction problem and the algebraic tools that allow to model the cameras and image features, I will also define the point and line Multiview varieties, and finally mention some algebraic results on the geometry of points and lines that can be reconstructed effectively.

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18/11/22 | Mar Giralt-Miron (UPC) | Chaotic dynamics, exponentially small phenomena and Celestial Mechanics

ABSTRACT:

A fundamental problem in dynamical systems is to prove that a given model has chaotic dynamics. One of the methods employed to prove this type of motions is to verify the existence of transversal intersections between the stable and unstable manifolds of certain objects. Then, there exists a theorem (the Smale-Birkhoff homoclinic theorem) which ensures the existence of chaotic motions.

In this talk we present a method to analyze the distance and transversality between certain stable and unstable manifolds when a small perturbation is added to an integrable system. In particular, we consider the case where the distance between manifolds is exponentially small. This implies that this difference cannot be detected by expanding the manifolds into a series with respect to the small perturbation parameter. Therefore, classical perturbation theory cannot be applied.

Finally, we apply these techniques to a celestial mechanics problem. In particular, we study the Lagrange point L 3 in the restricted planar circular 3-body problem.

Manuel Molano

Gerard Farré

Giovanni Dalmasso