12 - MLPDES25: Replicator dynamics on a network [ID:57454]
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And thank you, I want to thank the organizer for inviting me here in this beautiful air

language that has a weather as good as south of Italy, as you know in Germany we have such

a good weather.

So today I will talk about replicator dynamic that is actually the model that Professor

Orlando just talked about but on a network.

And this research is a part of a project, of a national project trying to study, I mean,

the biodiversity into the Mediterranean Sea.

The point is that, as I think all of you know, in the Mediterranean Sea and honestly in all

the seas around the globe, you have that there are a lot of species that are non-indigenous,

right?

Meaning that there are a lot of species that somehow were brought into that zone.

And the point is try to, the point of this project is, you know, try to first explain

how and why indigenous species are, we have so many indigenous species in those seas

and how to protect those species.

So with our workgroup we are starting to, you know, take some maps in order to understand

where are the zones that need to be protected in a sense.

And as you can see in this map, you have a lot of reserves that are zones in which you

have the majority of biodiversity indeed within the Mediterranean Sea.

I mean, Barry is in the hill.

No, no, I mean, everyone knows, everyone knows where Barry is.

Barry Caputmundi, you know.

Barry is in the hill of Italy, right?

I mean, my arm is too short, I cannot, goes there, but Barry is over there.

Okay.

And I mean, those zones are the biological reserves you ideally want to protect.

The interesting thing is that you can completely overlap this map with the ship routes and

with the most used routes and ports.

Okay.

This is not, you know, just a casualty.

As you know, for example, the blue crab, okay, it was brought from abroad into the Mediterranean

Sea through a ship.

It's indigenous, it's an alien into the Mediterranean Sea and now the Mediterranean Sea is its preferred

habitat.

But it was put into the Mediterranean Sea from a ship.

So this is quite usual, I mean, in terms of biological exchange.

Now the idea is to build a network in which every node of the network is a biological

reserve and is regulated by the replicator dynamics by Professor Orlando with a transport

term that is an extra reserve function that will let the species move back and forth from

a node to another.

Indeed, you have to consider that for all the properties of the replicator model that

Gianluca just told us, this function cannot be any function.

It needs to move the species from the simplex to the simplex.

It's a map between, I mean, from the standard simplex in R to the N to the standard simplex

in R to the N.

In this way, we build a model of N species consisting of N-V nodes connected by N-E edges

or roots.

That is the same, I mean.

For the transport term, we kept two functions.

The first one that is quite simple, I mean, is just a linear function exchanging species

from a node to another proportionally to the difference between the K-species in this node

Presenters

Dr. Alessandro Coclite Dr. Alessandro Coclite

Zugänglich über

Offener Zugang

Dauer

00:22:59 Min

Aufnahmedatum

2025-04-29

Hochgeladen am

2025-04-29 16:44:04

Sprache

en-US

#MLPDES25 Machine Learning and PDEs Workshop 
Mon. – Wed. April 28 – 30, 2025
HOST: FAU MoD, Research Center for Mathematics of Data at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen – Bavaria (Germany)
 
SPEAKERS 
• Paola Antonietti. Politecnico di Milano
 • Alessandro Coclite. Politecnico di Bari
 • Fariba Fahroo. Air Force Office of Scientific Research
 • Giovanni Fantuzzi. FAU MoD/DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
 • Borjan Geshkovski. Inria, Sorbonne Université
 • Paola Goatin. Inria, Sophia-Antipolis
 • Shi Jin. SJTU, Shanghai Jiao Tong University 
 • Alexander Keimer. Universität Rostock
 • Felix J. Knutson. Air Force Office of Scientific Research
 • Anne Koelewijn. FAU MoD, Friedrich-Alexander-Universität Erlangen-Nürnberg
 • Günter Leugering. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg
 • Lorenzo Liverani. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg
 • Camilla Nobili. University of Surrey
 • Gianluca Orlando. Politecnico di Bari
 • Michele Palladino. Università degli Studi dell’Aquila
 • Gabriel Peyré. CNRS, ENS-PSL
 • Alessio Porretta. Università di Roma Tor Vergata
 • Francesco Regazzoni. Politecnico di Milano
 • Domènec Ruiz-Balet. Université Paris Dauphine
 • Daniel Tenbrinck. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg
 • Daniela Tonon. Università di Padova
 • Juncheng Wei. Chinese University of Hong Kong
 • Yaoyu Zhang. Shanghai Jiao Tong University
 • Wei Zhu. Georgia Institute of Technology
 
SCIENTIFIC COMMITTEE 
• Giuseppe Maria Coclite. Politecnico di Bari
• Enrique Zuazua. FAU MoD/DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
 
ORGANIZING COMMITTEE 
• Darlis Bracho Tudares. FAU MoD/DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
• Nicola De Nitti. Università di Pisa
• Lorenzo Liverani. FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
 
Video teaser of the #MLPDES25 Workshop: https://youtu.be/4sJPBkXYw3M
 
 
#FAU #FAUMoD #MLPDES25 #workshop #erlangen #bavaria #germany #deutschland #mathematics #research #machinelearning #neuralnetworks

Tags

Erlangen mathematics Neural Network PDE Applied Mathematics FAU MoD Partial Differential Equations Bavaria Machine Learning FAU MoD workshop FAU
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