Free Meshless CFD Conference, AI Agents in Engineering Design & Physics-Informed Neural Nets
๐ Continuous improvement is better than delayed perfection.
Siemensโ Digital Thread: Connecting Design & Simulation - Bob Ransijn | Podcast #158
Free Particleworks Experience 2025 - The European Conference on Meshless CFD
Join engineers, simulation experts, and R&D professionals in Munich, Oct 8โ9, 2025, for two days of cutting-edge insights and networking.
Highlights:
E-Mobility & Thermal Management โ 90% faster EV simulation cycles, with demos from Dumarey Group, R&DCFD & ZF Group
Drivetrain & Gearbox Design โ WITTENSTEIN, SDF & SKF on lubrication, drag optimization & gearbox loss estimation
Industrial Process Optimization โ JKU Linz, SADEN & TotalEnergies on drying, startup modeling & oil aeration
What to expect:
Exclusive preview of the new software release
1-on-1 meetings & live demos
Networking dinner with industry leaders
๐ Munich, Germany
๐
October 8โ9, 2025 | 8:30โ17:30
๐ Free registration โ seats limited
๐ป ๐๐ ๐๐ด๐ฒ๐ป๐๐ ๐ถ๐ป ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด ๐๐ฒ๐๐ถ๐ด๐ป
In this work, the authors introduce the first multi-agent system for car design, integrating:
๐น ๐๐ผ๐๐ป๐ฑ๐ฎ๐๐ถ๐ผ๐ป ๐บ๐ผ๐ฑ๐ฒ๐น๐ (GPTs, Stable Diffusion) for language and vision
๐น ๐๐ฒ๐ผ๐บ๐ฒ๐๐ฟ๐ถ๐ฐ ๐ฑ๐ฒ๐ฒ๐ฝ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด models trained on physics-based simulations
๐น ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐๐ that connect seamlessly with engineering tools
From a ๐ต๐ฎ๐ป๐ฑ-๐ฑ๐ฟ๐ฎ๐๐ป ๐๐ธ๐ฒ๐๐ฐ๐ต โ to ๐ฝ๐ต๐ผ๐๐ผ๐ฟ๐ฒ๐ฎ๐น๐ถ๐๐๐ถ๐ฐ ๐ฟ๐ฒ๐ป๐ฑ๐ฒ๐ฟ๐ถ๐ป๐ด๐ โ to ๐ฏ๐ ๐๐ต๐ฎ๐ฝ๐ฒ ๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป/๐ฟ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น โ to ๐ต๐ถ๐ด๐ต-๐พ๐๐ฎ๐น๐ถ๐๐ ๐๐๐ ๐บ๐ฒ๐๐ต๐ฒ๐ โ to ๐ฟ๐ฒ๐ฎ๐น-๐๐ถ๐บ๐ฒ ๐ฎ๐ฒ๐ฟ๐ผ๐ฑ๐๐ป๐ฎ๐บ๐ถ๐ฐ ๐ฒ๐๐ฎ๐น๐๐ฎ๐๐ถ๐ผ๐ป, this framework shows how AI agents can seamlessly connect creativity with performance.
๐ฌ Paper: https://arxiv.org/abs/2503.23315
๐ DrivAerNet++: https://github.com/Mohamedelrefaie/DrivAerNet
๐ป 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.
๐ฆ ฮฆ-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 ฮฆ-SO discovering the analytical expression of a damped harmonic oscillator.
๐ Repo: https://github.com/WassimTenachi/PhySO
๐๏ธPhysics-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.
๐ฆ CFD Essentials: Lecture 6 - The Mechanics of Turbulent CFD
Philippe Spalart discusses the mechanics of running a turbulent CFD simulation:
The pre-processing steps
Generating grids
Obtaining solutions
Understanding the solution and using it for engineering purposes
๐ฌ Video of the Week
๐ป 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.
๐ 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 skippedโyouโll dive into math, theory, and practical applications. Everything is clearly explained in plain English.
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Keep engineering your mind! ๐ง
Jousef