Physics-Informed Machine Learning and Hybrid Modelling

Organizers

Bernhard Geiger*,+, Manfred Mücke#, Stefan Posch^

* Signal Processing and Speech Communication Laboratory, Graz University of Technology
+ Know Center Research GmbH
# Materials Center Leoben Forschung GmbH
^ Institute of Thermodynamics and Sustainable Propulsion Systems at Graz University of Technology

Workshop Description

The objective of this workshop is to present, explore, and critically discuss recent advancements in the rapidly evolving field of physics-based machine learning (PIML) and hybrid modeling. This interdisciplinary domain merges traditional physics-driven numerical methods with modern machine learning techniques, aiming to improve model fidelity, reduce computational cost, and enhance generalizability across a wide range of scientific and engineering problems such as fluid dynamics, solid mechanics, communications, or computational medicine.

Hybrid modeling, in particular, leverages the strengths of both paradigms—combining first-principles models with machine learning—to overcome limitations inherent in purely data-driven or purely mechanistic approaches. A fundamental challenge in hybrid modeling is to understand the propagation of errors and uncertainties of the (data-driven or first principles) parts, and how this affects the qualitative and quantitative behavior of the hybrid model. Complementary to hybrid modeling, PIML utilizes first-principles knowledge in the creation of machine learning models, influencing data selection, model parameterization, or learning itself via regularization. The workshop shall serve as a platform to connect developments in fundamental theory, algorithmic innovation, and application-driven research.

We invite contributions addressing the following topics:

  • Hybrid modeling approaches combining laws of physics and machine learning
  • Neural-enhanced algorithms and model-based deep learning
  • Novel physics-informed training objectives
  • Model parameterization approaches based on prior knowledge
  • Methods for constructing probabilistic hybrid or PIML models
  • Domain-aware training data creation/selection
  • Applications of PIML and hybrid models in science and engineering
  • Uncertainty and error propagation in hybrid models
  • Fundamental trade-offs in PIML and hybrid modeling (energy-performance, complexity-accuracy, etc.)

In addition to original research, we also explicitly invite extended abstracts summarizing already published results or work in progress.

An important aim of this workshop is to connect researchers in the field of PIML and hybrid modeling, and to thus establish a strong community in this field. Being the first workshop of its kind at AIRoV, we aim to identify the needs and common research interests of this community and to develop plans for joint collaborations during a discussion session.

Tentative Workshop Schedule

  • Welcome by the organizers, introductory lecture, and workshop outline: 30 mins
  • Keynote: 30 mins
  • Contributed Talks 1-2: 20 mins each
  • Coffee Break: 20 mins
  • Contributed Talks 3-5: 20 mins each
  • Spotlight Talks 1-8: 5 mins each
  • Coffee Break: 20 mins
  • Discussion Session: 120 mins

Paper Submission

Please refer to the Call for Papers for details regarding the double blind submission.

Important Dates

  • Paper submission deadline: 2026-02-28
  • Notification of acceptance: 2026-03-13

Contact

E-Mail: geiger@tugraz.at