Hello, my name is Oliver Hennigh, and this is my Aigrant proposal.
I am working on a project that uses deep learning to predict steady-state fluid flow faster than conventional flow solvers and then applying this to flow
optimization problems. So far, I have reimplemented the original paper with several improvements and laid the groundwork for how to tackle flow optimization.
There are two aspects of my project, the first being a research aspect
I am investigating techniques that make use of the differentiable nature of deep neural networks to Optimize boundary conditions and things like drag and lift.
Here we see a figure that illustrates this idea. The flow network predicts flow from boundaries. The boundary network generates a boundary from
parameters, in this case the params indicate height of the circle at various angles.
By following the red arrows we see that the boundary Params can receive a gradient with respect to the values of drag and lift.
We can then use these gradients to minimize or maximize these values. In this video
we see the boundary params learning the object that minimizes the drag while maximizing the lift.
This is the main aspect of my research however I will also investigate networks that require low working memory and make use of an
unsupervised loss function seen here. This portion of my project will culminate in a paper.
The second aspect of my project is to create a powerful open source library for predicting steady-state fluid flow.
Taking a step back from this particular project, the really exciting thing about learning to emulate physics simulations with Neural networks is it has
the potential to greatly reduce the computational
requirements for such simulations. This would give people without resources access to large simulations and push the bounds on what can be done with
supercomputers. With this in mind and working on open source library with pre-trained models and examples on how to generate flow simulations.
I would like to have this in a slick pip installable form along with a demo of a large 3D
simulation. The goal of this library is to give anyone access to fast accurate fluid simulations. I just finished a similar project
and will now devote my full time to this one
I estimate this project will take around 4 months however the Grant will support me for nine months
I don't have definite plans for my next projects however I am interest in applying neural networks to other computational physics domains, in particular
I'm interested in particle in cell plasma codes and computational chemistry. Thanks for watching
