FairCFD explores new ways to make fluid simulations faster, lighter, and more responsible — without sacrificing accuracy.
Our approach combines:
- Physics-based methods: stability analysis, reduced models, adjoint solvers.
- Data-driven tools: machine learning, sparse modeling, Bayesian inference.
- Hybrid strategies: mixing physics and data for robust, low-cost simulation.
- Efficient optimization: solving complex problems using smart gradients and AI.
A unique part of the project:
We work together to define what sustainable simulation really means — not just in numbers, but in practice.
You’ll contribute to cutting-edge research while shaping how science is done in the digital and ecological age.