AI in MATLAB, PINN Simulations & Deriving 3D Rigid Body Physics
📚 “The whole purpose of education is to turn mirrors into windows” – Sydney J. Harris
🎙️AI in MATLAB & Simulink & Model-Based Design
💻 Multi-Scale Simulations with PINNs
Can we carry out multi-scale simulations with physics-informed neural networks (PINNs)? PINNs often struggle to solve multi-scale problems, and in our new paper, we take steps to overcome this by combining them with multiple levels of domain decompositions.
TLDR
1) domain decomposition helps PINNs train faster and
2) multiple levels of domain decompositions facilitate communication between subdomains, further accelerating convergence. Below is an animation of our method for solving a 2D Laplacian problem with a highly multi-scale solution. See our paper and code for more!
👉 Paper & Code
💻 On machine learning methods for physics
Machine learning methods will fundamentally transform the landscape of physical simulation.
Physical modeling allows precise and simple descriptions of nature, yet large-scale simulations of these models can be computationally expensive. Traditional numerical methods have dominated these efforts for most of the last century. However, new paths to modeling macroscopic physics have opened with recent advancements in hardware, machine learning methods, and data collection strategies. This blog reviews some of the most promising new approaches and discusses the authors preferences in this broader context.
💦 MegaFlow2D: A Parametric Dataset for Machine Learning Super-resolution in Computational Fluid Dynamics Simulations
This paper introduces MegaFlow2D, a dataset of over 2 million snapshots of parameterized 2D fluid dynamics simulations of 3000 different external flow and internal flow configurations. It's worth noting that, simulation results on both low and high mesh resolutions are provided to facilitate the training of machine learning (ML) models for super-resolution purposes. This is the first large-scale multi-fidelity fluid dynamics dataset ever provided. We build the entire data generation and simulation workflow on open-source and efficient interfaces that can be utilized for a variety of data samples according to the user's specific needs. Finally, we provide a use case to demonstrate the potential value of the MegaFlow2D dataset in applications related to error correction.
💻 Deriving 3D Rigid Body Physics and implementing it in C/C++
💻 Engineering Tool of the Week – OOFEM
OOFEM is free finite element code with object oriented architecture for solving mechanical, transport and fluid mechanics problems that operates on various platforms.
📚Book of the Week
Modern Fortran by Milan Curcic 📚
Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light.
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Keep engineering your mind! 🧠
Jousef