Plenary Speakers

ETH Zurich, Switzerland

Physics-Enhanced Machine Learning for Monitoring & Twinning | An Exercise in Balance

ETH Zürich, Switzerland

Machine learning, data and physics for constitutive material modeling

Brown University, USA

Deep Neural Operators as Foundation Models for Digital Twins

Ludwig-Maximilians-Universität München, Germany

Trustworthy and Sustainable AI: From Mathematical Foundations to Next Generation AI Computing

University of Washington, USA

Modern Sensing and Physics Learning with Shallow Recurrent Decoders

Semi- Plenary Speakers

ENS Paris-Saclay, France

Data assimilation from hybrid twins for the practical real-time monitoring of complex engineering systems

University of Cambridge, United Kingdom

Statistical Finite Elements: A Bayesian Perspective on Digital Twinning

Stanford University, USA

A Theoretical Framework for Digital Twinning: Enhancements in Structural Health Monitoring

Umea University, Sweden

Solving Inverse and Ill-Posed Problems Using Stabilized Finite Elements, Data, and Machine Learning

Politecnico di Milano, Italy

Deep learning & reduced order modeling: opportunities, challenges & perspectives

University of the Basque Country (UPV/EHU), Spain

The challenges of integrating neural networks for solving parametric PDEs

University of Pennsylvania, USA

Digital Twins for the Earth System

Universite Gustave Eiffel, France

Machine learning-based multiscale fracture modelling