# Korali: Efficient and Scalable Software Framework for Bayesian Uncertainty Quantification and Stochastic Optimization

arXiv: https://arxiv.org/abs/2005.13457 · HTML: https://mlsanity.com/paper/2005.13457/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 4 of 4)

- [High Throughput Training of Deep Surrogates from Large Ensemble Runs](https://mlsanity.com/paper/2309.16743/) (2023)
- [Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2305.10548/) (2023)
- [Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning](https://mlsanity.com/paper/2404.02171/) (2024)
- [Controlling Topological Defects in Polar Fluids via Reinforcement Learning](https://mlsanity.com/paper/2507.19298/) (2025)

## Abstract (arXiv)

We present Korali, an open-source framework for large-scale Bayesian uncertainty quantification and stochastic optimization. The framework relies on non-intrusive sampling of complex multiphysics models and enables their exploitation for optimization and decision-making. In addition, its distributed sampling engine makes efficient use of massively-parallel architectures while introducing novel fault tolerance and load balancing mechanisms. We demonstrate these features by interfacing Korali with existing high-performance software such as Aphros, Lammps (CPU-based), and Mirheo (GPU-based) and show efficient scaling for up to 512 nodes of the CSCS Piz Daint supercomputer. Finally, we present benchmarks demonstrating that Korali outperforms related state-of-the-art software frameworks.