Next year, on April 28 – 30, 2025, our FAU MoD, Research Center for Mathematics of Data is hosting the “Machine Learning and PDEs” workshop (MLPDES25) supported by the FAU DCN-AvH, Chair for Dynamics, Control, Machine Learning and Numerics, the AFOSR, Air Force Office of Scientific Research, PoliBa, Politecnico di Bari and the Alexander von Humboldt Stiftung/Foundation organized from April 28 to 30, 2025 at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg in Erlangen – Bavaria (Germany).
This international workshop brings together researchers from across Europe and the United States to explore the growing connection between Machine Learning (ML) and Partial Differential Equations (PDEs)—two core fields in modern mathematics that are now developing a dynamic, mutually beneficial relationship. ML methods are increasingly used to simulate and solve complex PDEs, such as those found in bio-mathematics and fluid dynamics. Meanwhile, techniques from PDE and control theory are helping us better understand and improve ML models.
#MLPDES25 Watch the Video teaser
With participants from diverse backgrounds, this event aims to establish a collaborative platform for experts to network, share insights, and drive progress in this exciting field. We’ll dive into recent theoretical advancements and applications, while also discussing ongoing challenges in areas such as:
• Control and PDE methods for universal approximation and data classification
• Mean field analysis of neural networks
• ML applications in traffic flow modeling and autonomous driving
• ML and numerical simulation in bio-mechanics and micro-fluidics
Join us as we bridge these fields, focusing on both foundational research and practical applications.
REGISTRATION
Registration is free but mandatory.
Registration link: https://dcn.nat.fau.eu/mlpdes25-registration/
*After the event, an attendance certificate will be sent by email (Non-academic credits).
SPEAKERS
• Paola Antonietti. Politecnico di Milano
Lecture: Machine Learning enhanced polytopal finite element methods
• Alessandro Coclite. Politecnico di Bari
Lecture: Replicator dynamics on a network
• Fariba Fahroo. Air Force Office of Scientific Research
Lecture: TBA
• Giovanni Fantuzzi. FAU MoD/DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
Lecture: Data-driven system analysis: Polynomial optimization meets Koopman
• Borjan Geshkovski. Inria, Sorbonne Université
Lecture: Many-particle systems perspective on Transformers
• Paola Goatin. Inria, Sophia-Antipolis
Lecture: Modern calibration strategies for macroscopic traffic flow models
• Alexander Keimer. Universität Rostock
Lecture: Optimal control of nonlocal conservation laws and the singular limit
• Anne Koelewijn. FAU MoD, Friedrich-Alexander-Universität Erlangen-Nürnberg
Lecture: SSPINNpose: Self-supervised learning of biomechanical variables without ground truth
• Miroslav Krstic. UC San Diego
Lecture: Neural Operators: Implementation enablers for PDE control
• Camilla Nobili. University of Surrey
Lecture: Quantification of enhanced dissipation and mixing for time-dependent shear flows
• Gianluca Orlando. Politecnico di Bari
Lecture: A comparison between peridynamic and classical waves
• Michele Palladino. Università degli Studi dell’Aquila
Lecture: Handling uncertainty in optimal control
• Gabriel Peyré. CNRS, ENS-PSL
Lecture: Transformers are universal in-context learners
• Alessio Porretta. Università di Roma Tor Vergata
Lecture: Diffusion effects in optimal transport and mean-field planning models
• Francesco Regazzoni. Politecnico di Milano
Lecture: Discovering the hidden low-dimensional dynamics of time-dependent PDEs with latent dynamics networks
• Domènec Ruiz-Balet. Imperial College
Lecture: Some remarks on matching measures with Machine Learning architectures
• Daniel Tenbrinck. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg
Lecture: Eigenvalue problems on graphs and hypergraphs
• Daniela Tonon. Università di Padova
Lecture: Hamilton-Jacobi equations on infinite dimensional spaces
• Yaoyu Zhang. Shanghai Jiao Tong University
Lecture: The condensation phenomenon of Deep Neural Networks
• Wei Zhu. Georgia Institute of Technology
Lecture: Structure-Preserving Machine Learning and Data-Driven structure discovery
AUDIENCE
This is a hybrid event (On-site/online) open to: Public, Students, Postdocs, Professors, Faculty, Alumni and the scientific community all around the world.
PROGRAM
#MLPDE25 Program details
#MLPDE25 Schedule
WHEN
Mon. – Wed. April 28 – 30, 2025 • 09:30H – 17:00H
This event at your local time
WHERE
On-site / Online
[On-site] FAU, Friedrich-Alexander-Universität Erlangen-NürnbergSenatssaal (Senate Hall) im Kollegienhaus
Universitätsstraße 15, 91054
Erlangen – Bavaria, Germany
How to get to Erlangen? [Online] Live-streaming link TBA
Scientific Committee
• Giuseppe Maria Coclite. Politecnico di Bari
• Enrique Zuazua. FAU MoD/DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
Organizing Committee
• Nicola De Nitti. EPFL, École Polytechnique Fédérale de Lausanne
• Lorenzo Liverani. FAU DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
• Darlis Bracho Tudares. FAU MoD/DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg
#MLPDES25 Watch the Video teaser
You might like:
• FAU MoD Lectures
• Upcoming events
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