Massively increasing the speed of fluid simulations

Fluid simulations are an essential tool used by engineers and scientists globally. However, they are currently slow and expensive, often taking months to run on hundreds of CPUs. At Vanellus, we are developing machine learning techniques to yield a 100x increase in the computational efficiency of fluid simulations. Industries benefiting from these efficiency gains would include aerospace, renewable energy and medical devices.

These improvements will also allow the exploration of spatial resolutions that are currently cost prohibitive. These machine learning techniques show promise for accelerating other simulations covering a range of phenomena such as electromagnetism, heat transfer and chemical reactions.

Kolmogorov turbulence simulated with ML accelerated code

Droplet impact simulation - Michael Negus

New research has opened a world of machine learning accelerated physics

In order for a simulation to accurately model a physical system, it must typically be run at a fine spatial resolution. These high-resolution simulations need a large amount of computational resources, and often practitioners resort to running coarse simulations which, whilst cheaper, suffer from reduced accuracy.

Researchers have recently developed a method to increase the accuracy of coarse resolution simulations, training a machine learning model to infer the fine-scale structure present in high resolution simulations. This method maintains the accuracy of high resolution simulations without their extreme computational costs.

Vanellus aim to build upon and commercialise this cutting-edge research to help a range of industries to slash their simulation costs.

Supported by:

The Team

Vanellus founder Laurence Cullen relaxing in an office chair.

Laurence Cullen

Previously:, Sensity, ARM.

With six years of experience in machine learning and software engineering across companies large and small, Laurence is focused on developing and deploying the core Vanellus technology.

In his free time, Laurence can be found tending his garden, growing gourmet mushrooms and bouldering. He also dreams of becoming a Master league StarCraft 2 player.

Vanellus founder Michael Negus.

Michael Negus

Currently: University of Oxford. Previously: Diamond.

Currently finishing his DPhil in modelling droplet impact at the Mathematical Institute, University of Oxford, Michael leads our research and scientific computing, extending the state of the art in machine learning accelerated CFD.

When not grappling with vector calculus, Michael enjoys making generative art with his pen plotter and trying to survive while playing Rust.