Plenary Speakers
Francisco Chinesta
Arts et Métiers Institute of Technology & CNRS, France
Physics-aware digital twins for advanced generative design and optimal complex systems operation
Elías Cueto
Universidad de Zaragoza, Spain
Recent advances in graph neural network technologies for learned simulation.
Kento Sato
RIKEN Center for Computational Science, Japan
FugakuNEXT and Beyond: Integrating AI, Data, and Simulation at Scale
Semi-Plenary Speakers
C-S (David) Chen
National Taiwan University, Taiwan
Deep Material Network for Multiscale and Multiphysics Simulation
Paola Cinnella
Institut Jean Le Rond D'Alembert, Sorbonne Université, France
Active Multi-Fidelity Learning for High-Accuracy Flow Analysis and Design
Kai Fukami
Tohoku University, Japan
Revealing unsteady flow physics with observable-augmented machine learning
Yuan Tong Gu
Queensland University of Technology, Australia
Modular Physics-Informed AI for Computational Mechanics: Enabling Next-Generation Digital Twins
Mayuko Nishio
University of Tsukuba, Japan
Digital twinning strategy for data assimilation performance analysis of civil structures
Ricardo Vinuesa
University of Michigan, USA
From explainable deep learning to foundation models: discovery and control
Karen E. Willcox
The University of Texas at Austin, USA
The Critical Role of Uncertainty Quantification in Predictive Digital Twins