Monday, November 13, 2017

Deep Learning to Accelerate Computational Fluid Dynamics

Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks
I posted about a surprising application of deep learning to accelerate topology optimization. The thing I like about that approach is it's a strategy that could be applied to accelerate many different solvers that we use to simulate all sorts of continuum mechanics based on partial differential equations (i.e. computational fluid dynamics, structural mechanics, electrodynamics, etc.). With a bit of help from Google I found a neat paper and project on github doing exactly that for a Lattice-Boltzmann fluid solver.

Friday, November 10, 2017

Deep Learning to Accelerate Topology Optimization

Topology Optimization Data Set for CNN Training
Neural networks for topology optimization is an interesting paper I read on arXiv that illustrates how to speed up the topology optimization calculations by using a deep learning convolution neural network. The data sets for training the network are generate in ToPy, which is an Open Source topology optimization tool.

Saturday, March 25, 2017

Innovation, Entropy and Exoplanets

I enjoy Shipulski on Design for the short articles on innovation. They are generally not technical at all. I like to think of most of the posts as innovation poetry to put your thoughts along the right lines of effort. This recent post has a huge, interesting technical iceberg riding under the surface though.
If you run an experiment where you are 100% sure of the outcome, your learning is zero. You already knew how it would go, so there was no need to run the experiment. The least costly experiment is the one you didn’t have to run, so don’t run experiments when you know how they’ll turn out. If you run an experiment where you are 0% sure of the outcome, your learning is zero. These experiments are like buying a lottery ticket – you learn the number you chose didn’t win, but you learned nothing about how to choose next week’s number. You’re down a dollar, but no smarter.

The learning ratio is maximized when energy is minimized (the simplest experiment is run) and probability the experimental results match your hypothesis (expectation) is 50%. In that way, half of the experiments confirm your hypothesis and the other half tell you why your hypothesis was off track.
Maximize The Learning Ratio

Tuesday, March 7, 2017

NASA Open Source Software 2017 Catalog


NASA has released its 2017-2018 Software Catalog under their Technology Transfer Program. A pdf version of the catalog is available, or you can browse by category. The NASA open code repository is already on my list of Open Source Aeronautical Engineering tools. Of course many of the codes included in that list from PDAS are legacy NASA codes that were distributed on various media in the days before the internet.