Objectives

Disruptive technologies such as artificial intelligence, machine learning, and emerging computational platforms are reshaping the landscape of computational science. At CSQV 2025, we will explore these developments through keynote presentations, panel debates, and open discussions. The aim is to understand how our community can best take advantage of these changes and play a leading role in shaping the future of computational science.

Themes

Scientific Machine Learning for Simulation — neural operators, inverse problems, reduced-order surrogates
Data-Driven Twins & Assimilation — online data integration, Bayesian inversion, live digital twins
Hybrid Physics–ML Modeling — multiscale, multiphysics, diOerentiable simulation workflows
Trustworthy & Scalable Simulation — UQ and reliability, verification/validation, error estimation, GPU/HPC simulation
Programming and AI-Supported Tools — modern scientific programming, machine learning libraries, and AI-assisted code development