Learning Physics from Data, Automated Machine Learning & Hyperganic
📚 “Teachers open the door, but you must enter by yourself.” Chinese Proverb
🎙️Hyperganic: The Future of Additive Manufacturing
💻 The Future of MEMS Multiphysics Simulation
This blog post will delve into the future of MEMS technology, and the essential role that advanced simulation tools play in its development. We’ll cover what MEMS are, the significance of simulation tools, current challenges, recent advancements, and future trends. The article emphasises the necessity of investing in advanced simulation technologies to ensure the continued growth and innovation of MEMS.
💻 Generative Design Mirrors Nature: ToffeeX’s Physics-Driven Approach
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Engineers often look to the latest technologies and methodologies to innovate. Yet, the most profound source of inspiration and ingenuity has been crafting solutions for billions of years — nature itself. Engineers are dedicated problem solvers, but we only have a fraction of the experience of the oldest and greatest designer. Just as physical laws guide evolution, our approach to generative design is driven by the principles of physics. Here, we explore how ToffeeX’s generative design mirrors nature.
💻 Model Objects Rolling Down a Ramp with Simscape Multibody Open in MATLAB Online
The model simulates four objects (a sphere, a hollow sphere, a cylinder, and a hollow cylinder) moving down a ramp. These four objects have been selected because they have very similar geometrical properties, but can have very different inertias. The objects have been modeled with the intention of isolating inertia as the primary variable affecting their motion down the ramp. All objects have the same radius and mass, but differ in their inertias. This approach allows for a focused exploration of how mass distribution within an object influences its rolling behavior.
💦 Φ-SO : Physical Symbolic Optimization - Learning Physics from Data
The Physical Symbolic Optimization package uses deep reinforcement learning to discover physical laws from data. Here is Φ-SO discovering the analytical expression of a damped harmonic oscillator.
🌎 Repo: https://github.com/WassimTenachi/PhySO
💻 Engineering Tool of the Week – Fluidity
Fluidity is an open source, general purpose, multiphase computational fluid dynamics code capable of numerically solving the Navier-Stokes equation and accompanying field equations on arbitrary unstructured finite element meshes in one, two and three dimensions.
📚Book of the Week
Automated Machine Learning in Action
Automated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. It’s written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. AutoML can even be implemented by machine learning novices! If you’re new to ML, you’ll appreciate how the book primes you on machine learning basics. Experienced practitioners will love learning how automated tools like AutoKeras and KerasTuner can create pipelines that automatically select the best approach for your task, or tune any customized search space with user-defined hyperparameters, which removes the burden of manual tuning.
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Keep engineering your mind! 🧠
Jousef