./episode_167.sh
๐ The more I read, the more I acquire, the more confident I know nothing.
๐ฅ ๐๐ฟ๐ฎ๐๐ต ๐๐ฒ๐๐ ๐ฝ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ถ๐ผ๐ป ๐ฎ๐ ๐๐ต๐ฒ ๐๐ฝ๐ฒ๐ฒ๐ฑ ๐ผ๐ณ ๐๐
The need for a high number of design iterations comes up for performance-critical applications such as automotive crash testing, where physical tests are very expensive and time-consuming.
In this animation, we can see the SimAI prediction results of a crash impact on a bracket. Imagine a scenario where youโre using a virtual validation approach to assess how to get the best safety and durability performance from a bumper. Using SimAI you can generate an AI model trained on ~50 crash simulations from Ansys LS-DYNA. This would enable your design team to predict the optimal bumper configuration for a new concept in less than a minute, with an error of less than 0.5%.
Ultimately, itโs about enabling designers to use fast and meaningful crash predictions to accelerate a productโs time-to-market. Innovating faster towards a safer vehicle.
๐กIntroduction to OpenFOAM: Finite Volume Discretization in OpenFOAM
๐๏ธ Finite Element Analysis in the Browser using Google Collab
๐จ Scientific Visualization: Python + Matplotlib
This book is organized into 4 parts. The first part considers the fundamental principles of the Matplotlib library. This includes reviewing the different parts that constitute a figure, the different coordinate systems, the available scales and projections, and the authors also introduce a few concepts related to typography and colors.
The authors then explore the different types of plot available and see how a figure can be ornamented with different elements. The third part is dedicated to more advanced concepts, namely 3D figures, optimization, animation and toolkits.
๐ย Can Physics-Informed Neural Networks beat the Finite Element Method?
TL;DR:ย โConsidering the solution time and accuracy, PINNs are not able to beat the finite element method in our study.โ
The recent success of deep neural networks at various approximation tasks has motivated their use in the numerical solution of PDEs. These so-called physics-informed neural networks and their variants have shown to be able to successfully approximate a large range of partial differential equations. So far, physics-informed neural networks and the finite element method have mainly been studied in isolation of each other.
๐ป Engineering Tool of the Week - SPARSELIZARD
Sparselizard is a high-performance, multiphysics, hp-adaptive, open-source C++ finite element library running on Linux, Mac and Windows. Aย fast algorithm for mesh-to-mesh interpolationย and a general implementation of theย mortar finite element methodย allows us to easily work with non-matching meshes and provide general periodic conditions. FEM simulations can be weakly or stronglyย coupled to lumped electric circuits.
๐Book of the Week
โI do like CFDโ
"I do like CFD, VOL.1"ย ย is aย book onย Computional Fluid Dynamics: the first one in a series to come. It is written by Dr. Katate Masatsuka,ย bringing those CFD know-how to the public.
๐ Meme of the Week
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For any business-related issues or collaborations, email me atย support@jousefmurad.com!
Keep engineering your mind! ๐ง
Jousef