Physics Meets AI, Meshless FEA, AI Agents & Generate CAD From Prompts
🧠 Education is a progressive discovery of our own ignorance.
💻 Physics Meets AI - Geometric Deep Learning with Altair's PhysicsAI
Live Deep Dive Session! 🚨 Discover how PhysicsAI by Altair enables fast, intuitive machine learning-driven physics predictions, empowering seamless design exploration and optimization with unparalleled efficiency and flexibility.
🏗️ Meshless FEA Podcast
🧠 White Paper on Generative AI Agents, Revealing the Future of Smart Assistants
Google recently released a detailed white paper exploring the development and capabilities of generative AI agents. This document explains how these intelligent agents utilize external tools to go beyond the capabilities of traditional language models and accomplish more complex tasks.
⚡ Generate CAD from Text Prompts
Text-to-CAD is an open-source prompt interface for generating CAD files through text prompts. Generate models that you can import into the CAD program of your choice.
The infrastructure behind Text-to-CAD utilizes our Design API and Machine Learning API to programmatically analyze training data and generate CAD files.
🧠 Fundamentals of Computational Engineering: Part 1 — A Bit of History
💻 First Mathematical Proof for Key Law of Turbulence in Fluid Mechanics
What if engineers could design a better jet with mathematical equations that drastically reduce the need for experimental testing? Or what if weather prediction models could predict details in the movement of heat from the ocean into a hurricane? These things are impossible now, but could be possible in the future with a more complete mathematical understanding of the laws of turbulence.
🌱 How to Read a Paper
Researchers must read papers for several reasons: to review them for a conference or a class, to keep current in their field, or for a literature survey of a new field. A typical researcher will likely spend hundreds of hours every year reading papers. Learning to effectively read a paper with this free resource.
🎬 Video of the Week
💻 Engineering Tool of the Week - Surrogate Modeling Toolbox
The surrogate modeling toolbox (SMT) is a Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and facilitates the implementation of additional methods.
SMT is different from existing surrogate modeling libraries because of its emphasis on derivatives, including training derivatives used for gradient-enhanced modeling, prediction derivatives, and derivatives with respect to the training data.
📚Book of the Week
Inside Deep Learning
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you’ll dive into math, theory, and practical applications. Everything is clearly explained in plain English.
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Keep engineering your mind! 🧠
Jousef