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🧠 The great aim of education is not knowledge but action.

Oct 23, 2022

Hey friends & nerds! 👋

Welcome to the Sunday Science Newsletter – in this newsletter we explore & discuss strategies, systems & tools that help us become better, smarter and more effective scientists.

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🚀 LIVE NOW - Master MATLAB & Simulink

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In 50+ lessons, you are going to learn the programming language MATLAB as well as Simulink to model physical systems. This is the course to get started with and prepare for your studies, an exam or even your thesis project.

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👉 Getting up to speed for your thesis or job!

👉 Including Quizzes, Exercises and solution files

🎙️ Becoming a NASA Engineer – Ravi Margasahayam

☁️ Simulation in the Cloud - Onshape Update

Check it out!

Onshape just released native simulation to analyze stress, displacement and factors of safety for assemblies under static loads leveraging the power of cloud computing. You’ve never seen simulation this Fast, Easy, and Accurate!

🆕 Simcenter STAR-CCM+ 2210 release will be live soon!

GPU acceleration is redefining traditional CFD design practices, and as GPU architecture continues to improve at a rapid pace, so too shall the boundaries of what is considered a practical level of fidelity for CFD simulations. i.e. higher fidelity CFD while you sleep! In Simcenter STAR-CCM+ 2210, the team continue to expand their GPU-enabled physics unlocking overnight high-fidelity CFD.

Full Blog

💻 Engineering Tool of the Week – COOLFluiD

The object-oriented HPC platform for CFD, plasma and multi-physics simulations whose development started in 2002 at the Von Karman Institute for Fluid Dynamics (www.vki.ac.be) in collaboration with the KU Leuven Center for mathematical Plasma Astrophysics (CmPA) dept. (https://wis.kuleuven.be/CmPA) is open.

GitHub Repo

📚 Book of the Week

Deep Learning with PyTorch

Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning.

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