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

Jun 04, 2023

Hey friends & nerds! 👋

Welcome to the Sunday Science Newsletter where we explore science, systems & tools that help us become smarter scientists.

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🤓📝 COMSOL, Inc. & Quanscient : Comparing Multiphysics Software Solutions

This blog will thoroughly compare COMSOL and Quanscient, exploring their features, capabilities and user interfaces.

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💻 Enterprise Resource Planing - Tim Felbinger

💻 Physics-Informed Neural Network - Free Class

The idea is very simple: add the known differential equations directly into the loss function when training the neural network.

This is done by sampling a set of input training locations () and passing them through the network. Next gradients of the network’s output with respect to its input are computed at these locations (which are typically analytically available for most neural networks, and can be easily computed using autodifferentiation). Finally, the residual of the underlying differential equation is computed using these gradients, and added as an extra term in the loss function.

Full Blog.

💻 CFD Lectures for Incompressible Flow

The present lecture notes are written to emphasize the mathematics of the Navier–Stokes (N.–S.) equations of incompressible flow and the algorithms that have been developed over the past 30 years for solving them.

Full Lecture Notes

🏎️ The Science Behind 'Somersault' Crashes & Dangerous Aero

Race car crashes can be horrifying, but none get close to the craziness of those witnessed at the 1999 Le Mans 24 hour race. Seemingly out of nowhere, cars can suddenly flip in the air, with some doing full 360s or others cascading off into or even over the barriers. Read in this blog post what measures modern race cars take to prevent it happening again👇

Blog Post

💻 Engineering Tool of the Week – deal.II — An Open Source Finite Element Library

deal.II is a C++ program library targeted at the computational solution of partial differential equations using adaptive finite elements. It uses state-of-the-art programming techniques to offer you a modern interface to the complex data structures and algorithms required.

Official Website

📚 Book of the Week

Deep Learning with PyTorch (My Favorite PyTorch Resource)

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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