IWR School 2025
"Machine Learning for Fundamental Physics"

September 15-19, 2025

Machine Learning is here to stay and is shaping the future of fundamental physics research. From optimal inference, over theory-inspired network architectures, to anomaly detection, representation learning and foundation models, a new generation of scientists is driving these exciting developments. This school aims to further strengthen technical expertise and foster new connections.

The 2025 IWR School on Machine Learning for Fundamental Physics is aimed at advanced PhD students specializing in scientific machine learning. We particularly encourage registrations from researchers with experience in scientific machine learning, as demonstrated by papers or preprints related to the topic of the school. The school takes place at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University September 15-19, 2025.

Target Audience

Advanced PhD students specializing in scientific machine learning

Topics

• Modern network architectures
• Precision and uncertainties
• Scientific foundation models
• Generative Networks
• Representation learning
• Optimal inference
• Quantum field theory and networks

Scientific Organizers: Tilman Plehn, David Shih, Caroline Heneka

Location: Mathematikon • Im Neuenheimer Feld 205 • 69120 Heidelberg, Germany

For more information and how to apply online please visit the website of the IWR School 2025.

"KI in Anwendung - "Steering Trustworthy AI-Systems in the Public Interest""

mit einem Impuls-Vortrag von Sarah Schwettmann • Transluce.org & MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)

14. Februar 2025 • 14:00 - 16:00

Thema:
Bedenken bezüglich der Vertrauenswürdigkeit und Transparenz großer KI-Modelle sind aktueller denn je. Lassen sich große Sprachmodelle nachvollziehbar verstehen und transparent steuern? Der Vortrag bietet spannende Einblicke in die Frontforschung zu KI sowie deren ethische, politische und praktische Herausforderungen.

Sprache:
Englisch (Übersetzung der Diskussion durch Moderation möglich)

Anmeldung:
Bis inkl. 10.02.2025 per E-Mail an Thomas Renkert (renkert@heideu.ph-heidelberg.de)

Im Anschluss:
Get-Together mit Kolleg*innen des IWR

Ort: Mathematikon • Konferenzraum (5. Stock) • Im Neuenheimer Feld 205, 69120 Heidelberg

IWR Colloquium Winter Semester 2024 / 2025

"Adjoint-based calibration of nonlinear stochastic differential equations"

Prof. Stefan Volkwein • Department of Mathematics and Statistics, University of Konstanz
January 29, 2025 • 16:15

To study the nonlinear properties of complex natural phenomena, the evolution of the quantity of interest can be often represented by systems of coupled nonlinear stochastic differential equations (SDEs). These SDEs typically contain several parameters which have to be chosen carefully to match the experimental data and to validate the effectiveness of the model. In the present paper the calibration of these parameters is described by nonlinear SDE-constrained optimization problems. In the optimize-before-discretize setting a rigorous analysis is carried out to ensure the existence of optimal solutions and to derive necessary first-order optimality conditions. For the numerical solution a Monte-Carlo method is applied using parallelization strategies to compensate for the high computational time. In the numerical examples an Ornstein-Uhlenbeck and a stochastic Prandtl-Tomlinson bath model are considered.

This is a joint work with Jan Bartsch and Robert Denk. The associated paper will appear in the Journal Applied Mathematics & Optimization.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom. For more information please visit the website of the colloquium.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

"Machine learning galore!"

January 16, 2025 • 16:30 - 18:00

Machine learning galore will feature lab presentations by PIs as well as scientific talks by junior scientists.

• Lab presentations:
  • Fruzsina Molnár-Gábor, Dominik Niopek, Björn Malte Schäfer
• Science talks:
  • N.N. (Molnár-Gábor lab): Title of the scientific lecture follows
  • Benedict Wolf (Niopek lab): ML-guided Engineering of Switchable Proteins
  • Tobias Röspel (Schäfer lab): Statistical mechanics meets Bayes inference and machine learning

To help plan the catering, please register for free by clicking here. (Deadline: January 13, 2025)

Scientific Machine Learning is a joint initiative from STRUCTURES and IWR aimed at fostering interactions within and development of the local machine learning community. Its portal, http://mlai.uni-heidelberg.de summarizes the many relevant events and news from across campus that would otherwise remain scattered across single institutions or fields.
The goals of the MLAI platform align with the STRUCTURES Cluster of Excellence's objective of driving research into the fundamental understanding of current and future machine learning, and with IWR’s aim to leverage machine learning to enable the solution of long-standing problems in the natural and life sciences, the engineering sciences, as well as the humanities.

Further information and links:

• MLAI homepage
• Machine Learning Talks on Campus – Information service and mailing list
• STRUCTURES Cluster of Excellence

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg

IWR Colloquium Winter Semester 2024 / 2025

"Cut Finite Element Methods (CutFEM)"

Prof. Sara Zahedi • Department of Mathematics, KTH, Stockholm, Sweden
January 15, 2025 • 16:15

In this talk, I will introduce Cut Finite Element Methods (CutFEM) for interface problems and present recent developments, such as mass conservation, within this class of unfitted finite element techniques. Finite Element Methods (FEM) are widely used to approximate solutions to partial differential equations (PDEs) in complex geometries, but they typically require conforming computational meshes. The goal of CutFEM is to allow interfaces or boundaries to intersect the computational mesh arbitrarily while preserving desirable properties of standard FEM. I will explain how CutFEM maintains accuracy and avoids ill-conditioning of linear systems, even with unfitted meshes. I will begin with stationary problems, and then present our approach for discretizing time-dependent PDEs in evolving domains, and demonstrate its application to simulations of surfactant dynamics in incompressible two-phase flow systems.

The IWR Colloquium will be held as an in-person event at the Mathematikon. In addition it will be streamed via Zoom. For more information please visit the website of the colloquium.

Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg