2023 BrainChip Discussion, page-3421

I see our mates at TCS been doing some work on CFDs and SNN with a presso at Dec 22 NeurIPS.

Looks like they intend to run their algo on Akida in future work.

Paper HERE

Stabilization and Acceleration of CFD Simulation by Controlling Relaxation Factor Based on Residues: An SNN Based Approach

Mithilesh Maurya, Dighanchal Banerjee, Sounak Dey, Dilshad Ahmad
TCS Research, India

Abstract
Computational Fluid Dynamics (CFD) simulation involves the solution of a sparse system of linear equations. Faster convergence to a physically meaningful CFD simulation result of steady-state physics depends largely on the choice of optimum value of the under-relaxation factor (URF) and continuous manual monitoring of simulation residues. In this paper, we present an algorithm for classifying simulation convergence (or divergence) based on the residues using a spiking neural network (SNN) and a control logic. This algorithm maintains optimum URF throughout the simulation process and ensure accelerated convergence of the simulation. The algorithm is also able to stabilize and bring back a diverging simulation to the converging range automatically without manual intervention. To the best of our knowledge, SNN is used for the first time to solve such complex classification problem and it achieves an accuracy of 92.4% to detect the divergent cases. When tested on two steady-state incompressible CFD problems, our solution is able to stabilize every diverging simulation and accelerate the simulation time by at least 10% compared to a constant value of URF.

Conclusion and Future Works. Our proposed solution is generic and can be integrated with any CFD tools. This automated process saves a lot of manual effort and time thereby making it beneficial for many industry scale CFD problems that run for days. Moreover, the SNN model can easily be retrained with additional data to improve the accuracy and to cater to different classes of problems.

In future, we intend to improve the existing fixed control logic to an adaptive URF controller varying with the SR value. We also intend to test the performance and power consumption of the SNN by running it on a real neuromoprhic hardware such as Intel Loihi [2, 9] or Brainchip Akida [1].