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📚 “Only the educated are free.” – Epictetus
💦 Fluid-Structure Interaction
💻 Practical Machine Learning for Engineers | Deep Dive
The Future of AI in Engineering: PINNs and More! In this session, we dive into the cutting-edge world of Machine Learning (ML) and its rapidly growing impact on mechanical and simulation engineering. Learn how solutions like Synera are changing the ML landscape through advanced algorithms and user-friendly pipelines, and discover when to leverage the power of AI in your projects.
Segments include:
👉 Practical ML Applications: When to use ML to enhance efficiency and when to pass due to complexity or cost.
👉 Breaking Down ML Barriers: Ways engineers can empower themselves with ML knowledge and resources.
👉 Future Tools: Copilots & ROMs: Understand how these innovations are setting new standards in engineering.
👉 Looking Ahead: Explore future trends, breakthroughs, and how Synera can shape the ML landscape.
💡 4 Myths about AI in CFD
Myth #1: Numerical models are the only solutions available to solve difficult engineering problems. AI is not accurate enough to be a good fit for CFD and engineering problems.
Reality: Thanks to new technological advances, AI/ML solutions now exist that work well on subsystems with very minimal prediction error. Groups such as our very own Simcenter Engineering & Consulting Services have worked with customers to use AI/ML in design predictions to minimize CFD simulations.
Myth #2: Data science is easy and there are open-source tools that companies can use.
Reality: Generic machine learning algorithms cannot handle customer-specific engineering problems. Different applications require different machine learning algorithms and further problem-specific refinement (for example shape detection techniques). Although there are widely available image recognition algorithms, companies must develop an in-house shape detection algorithm to detect various shapes or components of a vehicle.
Myth #3: AI doesn’t require people to run it.
Reality: The value of AI lies in its ability to augment the capabilities of computer engineers and domain experts. By performing the monotonous and repetitive tasks, it helps engineers focus on solving more complex problems using CFD tools, such as Simcenter STAR-CCM+, to generate more meaningful simulation data for future predictions.
Myth #4: The more data, the better.
Reality: AI in CFD needs smart engineering data, such as Key Performance Index (KPI), relevant to the problem, to succeed. Our Simcenter Engineering Services group uses feature engineering technologies to achieve data reduction from simulation data to enable AI to deliver high-quality solutions.
💦 EP2 - Dr Florian Menter - CFD Turbulence Modelling Pioneer
Dr. Florian Menter discusses his journey in the field of computational fluid dynamics (CFD) and the development of the K-Omega SST model. He shares his experiences working at NASA Ames and the collaborative environment in the CFD community. Florian also talks about his decision to return to Germany and his role in the early days of what would be become ANSYS. Florian Menter discusses the birth and development of the SST turbulence model, the challenges of transition modeling, and the future of RANS models. He also explores the potential of machine learning in CFD and shares advice for young researchers.
📚 Physics-Informed Neural Networks (PINNs) - An Introduction - Ben Moseley
Physics-informed neural networks (PINNs) offer a new and versatile approach to solving scientific problems by combining deep learning with known physical laws. Such networks are able to simulate physical systems, invert for their underlying parameters, and even discover underlying physical laws themselves.
In this introductory workshop and live coding session, we will cover the basic definition of a PINN, its pros and cons compared to traditional scientific techniques, and some of the state-of-the-art research in the field.
💻 Business Class – Content Repurposing with AI
👉 What is Content Repurposing?
Most top marketers don't create content from scratch. Learn how to build a growth engine that leverages your existing co
👉 How AI Gives You Marketing Superpowers
Learn the key tools I use to create 10+ pieces of content every week!
👉 Step-by-Step Prompts
Learn about the PODS framework & Hands-on demo of generating LinkedIn posts
💻 Engineering Tool of the Week – Wildkatze
Wildkatze is a general purpose three-dimensional CFD software package with robust Finite Volume and Finite Difference solvers, preprocessing module, and Multiphysics models for a wide range of industrial problems.
📚Book of the Week
The Lattice Boltzmann Method: Principles and Practice
This book is an introduction to the theory, practice, and implementation of the Lattice Boltzmann (LB) method, a powerful computational fluid dynamics method that is steadily gaining attention due to its simplicity, scalability, extensibility, and simple handling of complex geometries. The book contains chapters on the method's background, fundamental theory, advanced extensions, and implementation.
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Keep engineering your mind! 🧠
Jousef