This two-day workshop is designed to provide you with hands-on experience using 4C & QUEENS, individually and in combination.

You will learn to independently

1. build and run complex multiphysics models in 4C,

2. apply modern (probabilistic) multi-query analysis techniques in QUEENS,

3. leverage the interoperability between 4C & QUEENS to address uncertainty in complex models.

Practical exercises will range from fundamental solid mechanics problems to fluid-structure interaction in 4C, and from basic parameter studies to advanced probabilistic analysis, including surrogate modeling, in QUEENS.

Each session is structured to encourage active learning, peer discussion, and direct interaction with experienced tutors.

The software will be provided through pre-configured environments that require minimal setup and prior knowledge.

This ensures a smooth, accessible entry into the 4C & QUEENS ecosystem, allowing you to focus on gaining practical experience with the tools - rather than troubleshooting installation issues.

By the end of the event, you will have gained first-hand experience that sets the stage for deeper exploration and long-term adoption of 4C & QUEENS.

The sessions will be led by the developers and expert users of 4C and QUEENS, who will share their insights from cutting-edge research and real-world applications. Their diverse expertise will offer participants a comprehensive view of how simulation and probabilistic modeling can be combined to solve previously intractable problems in science and engineering.

4C (Comprehensive Computational Community Code) is a high-performance research code for solving a wide range of physical problems described by ordinary or partial differential equations. It offers advanced simulation capabilities for single-field and multiphysics systems, including solids, fluids, and scalar transport phenomena.

Designed with modularity in mind, 4C supports the development of custom numerical methods and new modeling strategies, making it ideal for researchers pushing the boundaries of simulation science.

4C is publicly available here: https://github.com/4C-multiphysics/4C

QUEENS (Quantification of Uncertain Effects in Engineering Systems) is a Python-based framework for solver-independent, multi-query analysis of large-scale computational models.

It enables parameter studies, uncertainty quantification, and Bayesian inverse problems, all within high-performance computing environments.

QUEENS emphasizes probabilistic thinking and abstracts away the complexity of managing large ensembles of simulations, offering a robust, reproducible, and scalable analysis platform.

QUEENS is publicly available here: https://github.com/queens-py/queens

Physical and probabilistic modeling are distinct yet complementary domains, each requiring specialized expertise.

The 4C and QUEENS frameworks form an interoperable ecosystem that tightly connects both fields: 4C enables high-fidelity modeling of single- and multiphysics systems, while QUEENS provides tools for simulation analytics and probabilistic modeling.

Their co-development supports independent modeling in each domain while allowing seamless integration of multiphysics simulations with advanced analysis techniques. This fusion empowers researchers to address complex, uncertain systems with greater insight and predictive accuracy.