PINNs in MATLAB, The New Simcenter STAR-CCM+ Release & Mathematics of Turbulent Flows
π Continuous improvement is better than delayed perfection.
ποΈPhysics-Informed Neural Networks (PINNs) - Conor Daly
Physics-Informed Neural Networks (PINNs) integrate known physical laws into neural network learning, particularly for solving differential equations. They embed these laws into the network's loss function, guiding the learning process beyond data fitting.
βοΈ High Performance Coldplates
Mathias, Syam, and Nicola will discuss the project in detail - from ideation and design to manufacturing and testing. Stay till the end for a live Q&A where theyβll be able to answer your questions.
π€ Simcenter STAR-CCM+ 2502 released! Whatβs new?
The latest update to Simcenter STAR-CCM+ 2502 brings pivotal enhancements across various domains, boosting simulation speed, enhancing model accuracy, and improving integration across different engineering disciplines. Key advancements include an efficient mesh motion technology for moving objects, faster vehicle thermal management and aerodynamics simulations, streamlined data exchange for E-Machines, and sophisticated methods for accurately modeling battery thermal runaway onset, corrosion and complex non-Newtonian fluid behavior.
π¦ Machine Learning in Fluid Dynamics
A curated list of awesome Machine Learning (Deep Learning) projects in Fluid Dynamics. Topics consist of Computational Fluid Dynamics (CFD), turbulence modeling, non-Newtonian fluids, Hemodynamics, PIV measurement, Geophysical fluid dynamics, Aeroelasticity, multiphase flow, etc.
β¨οΈ Fluid Mechanics Series
This collection of videos was created about half a century ago to explain fluid mechanics in an accessible way for undergraduate engineering and physics students. I find that no other series of videos has explained the basics of fluid mechanics better than this one by the National Committee for Fluid Mechanics (those national committees gotta be good for something...)
π¬ Video of the Week
π» Engineering Tool of the Week β Ξ¦Flow - Open-Source Simulation Toolkit
Ξ¦Flow is an open-source simulation toolkit built for optimization and machine learning applications. It is written mostly in Python and can be used with NumPy, PyTorch, Jax or TensorFlow.
πBook of the Week
An Introduction to Statistical Learning: with Applications in R
The 2nd edition of An Introduction for Statistical Learning (with R examples) is out! The book includes code examples with #R.
The second edition includes the following topics:
- Sparse methods for classification and regression
- Decision trees
- Boosting
- Support vector machines
- Clustering
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Keep engineering your mind! π§
Jousef