MLPDES26, Machine Learning and PDEs Workshop (2026)

Date: Mon.-Wed. June 22 – 24, 2026
Event: MLPDES26, Machine Learning and PDEs Workshop (2026)
Host: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)
Event type: Hybrid (On-site / Online)
Registration form (on-site participation only): dcn.nat.fau.eu/registration-mlpdes26

Next Spring, our FAU MoD, Research Center for Mathematics of Data is hosting the “Machine Learning and PDEs” workshop 2026 (MLPDES26) supported by the FAU DCN-AvH, Chair for Dynamics, Control, Machine Learning and Numerics, PoliBa, Politecnico di Bari and the Alexander von Humboldt Stiftung/Foundation organized from June 22 to 24, 2026 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.

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.

Join us as we bridge these fields, focusing on both foundational research and practical applications!

Poster of the #MLPDES26

REGISTRATION

Free but mandatory (on-site participation only)
Registration form: dcn.nat.fau.eu/registration-mlpdes26

*After the event, an attendance (on-site) certificate will be sent by email (Non-academic credits).

SPEAKERS

• Riccardo Adami. PoliTO, Politecnico di Torino.
Lecture: A mathematical model for the Einstein-Podolsky-Rosen phenomenon

• Antonio Alvarez. UAM, Autonomous University of Madrid and FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg
Lecture: Perceptrons and localization of attention’s mean-field landscape

• Harbir Antil. GMU, George Mason University
Lecture: Digital Twins and Beyond: A PDE-Constrained Optimization Perspective

• Luigi Berselli. Unipi, University of Pisa
Lecture: Womersley type flows for a non Newtonian fluid with variable exponent

• Maria Bruna. University of Oxford
Lecture: Microscopic interactions and macroscopic PDEs: beyond the mean-field paradigm

• Giuseppe Buttazzo. Unipi, University of Pisa
Lecture: Optimal data for elliptic PDEs

• Gonzalo Cao Labora. EPFL, École Polytechnique Fédérale de Lausanne
Lecture: Discovery of unstable singularities

• Elena Celledoni. NTNU, Norwegian University of Science and Technology
Lecture: Structure preservation and Deep Learning for Learning Mechanical Systems from Data

• Annalisa Cesaroni. Unipd, Università degli Studi di Padova
Lecture: Analysis of commuting times in a linear city

• Ulrik Skre Fjordholm. University of Oslo
Lecture: The zero-noise limit for hyperbolic conservation laws

• Ingenuin Gasser. Universität Hamburg
Lecture: Mathematical models in the context of renewable energies

• Sam G. Krupa. ENS Paris, École Normale Supérieure – PSL
Lecture: Are L^∞ solutions to hyperbolic systems of conservation laws unique?

• Xue-Mei Li. EPFL, École Polytechnique Fédérale de Lausanne
Lecture: Rough PDEs, Long-Range Dependence, and Multi-Scale Dynamics

• Francesco Maddalena. Poliba, Politecnico di Bari
Lecture: Multiple Scales in a Nonlocal Evolution Equation of Continuum Mechanics

• Lorenzo Pareschi. Heriot Watt University and Università degli studi di Ferrara
Lecture: Structure-Preserving Neural Surrogates for Uncertainty Quantification in Plasma Physics

• Katharina Schratz. Sorbonne Université
Lecture: Resonances as a Computational Tool

• Emil Wiedemann. FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg
Lecture: Beyond Incompatibility: Using Optimal Transport for Algorithmic Fairness

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

#MLPDES26 Schedule

WHEN

Mon.-Wed. June 22 – 24, 2026 • 09:30H – 17:00H
This event at your local time

WHERE

On-site / Online

[On-site] FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg
Senatssaal (Senate Hall) im Kollegienhaus
Universitätsstraße 15, 91054
Erlangen – Bavaria, Germany
How to get to Erlangen?

[Online] Live-streaming @FAUtv
https://www.fau.tv/clip/id/63196

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 MoD | DCN-AvH, Friedrich-Alexander-Universität Erlangen-Nürnberg

This event @LinkedIn

You might like:
• MLPDES25, 1st. Edition of the Machine Learning and PDEs Workshop (2025)
• Upcoming events
• FAU MoD Lectures
• FAU MoD Courses & Workshops

Last update: 13.05.2026

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