./episode_39.sh

Don’t tell people your plans.

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CFD "Superman" Cyclist

The analysis is performed by Computational Fluid Dynamics (CFD) simulations with the 3D RANS equations and the Transition SST k-ω model. The simulations are validated wind tunnel measurements. The results are analyzed in terms of frontal area, drag area and surface pressure coefficient.

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Simple Multi-Layer Perceptron in Julia

In this example, we create a simple multi-layer perceptron (MLP) that classifies handwritten digits using the MNIST dataset. A MLP consists of at least three layers of stacked perceptrons: Input, hidden, and output. Each neuron of an MLP has parameters (weights and bias) and uses an activation function to compute its output.

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Favorite Computer Science Papers

A curated list of great computer science papers that I’ve enjoyed reading and re-reading over the past years.

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Upcoming Podcast

For the upcoming podcast I am very happy to welcome Bernat Font to my show!

Dr. Bernat Font obtained his MEng in Aeronautical Engineering from the Universitat Politecnica de Catalunya (Spain) together with an MSc in CFD from Cranfield University (UK) in a double degree program in 2015. He is currently a Postdoctoral researcher at the Barcelona Supercomputing Center (BSC-CNS) working on NextSim, an European project to develop a fast numerical flow solver for aerodynamic applications.

In this podcast, we talked about:

  • Pros & Cons of doing a PhD 📚

  • Turbulence 🌀

  • 3D & 2D Turbulence 🌪️

  • Machine Learning for CFD 💻

  • and much more…

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Book of the Week

Trust in Machine Learning

You’ll learn how to handle the inevitable distribution shift between training and operating data, measure and mitigate bias and unfairness, and be robust to deliberate sabotage from adversarial attacks. Interpretability techniques demystify how your ML makes its decisions, and transparent reporting mechanisms ensure your whole pipeline is open and explainable. You’ll discover the dark side of AI such as filter bubbles and malicious deepfakes, and learn how to prevent unintended consequences from arising. Finally, you’ll see how machine learning can be used with real benevolence to empower nonprofits and do social good.

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Closing Remarks

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See you next week and in the meantime, make sure to keep engineering your mind!

Jousef