AI, PINNs, and Real-Time Design | AI in Engineering #1

Warmly welcome to the first edition of our newsletter!?

Here, we will keep you at the forefront of the latest developments in AI, Engineering, and industry advancements and innovations.

In this newsletter, you will have the opportunity to:

• Learn about the most recent engineering technological breakthroughs
• Explore the latest resources in the field of AI for Engineering
• Stay in the loop with our newest projects and announcements

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AI Accelerated Engineering: Real-Time Results for Real-World Engineering Challenges

?? Register here: https://www.navasto.de/webinars/data-driven-engineering-approaches/

In today's fast-paced world, the demand for high-quality products within tight timelines places great pressure on engineering companies to innovate and deliver swiftly.?

Integrating AI-accelerated engineering has marked a turning point in response to this challenge.?

Our recent webinar on AI accelerated engineering: Real-Time Results for Real-World Engineering Challenges, explores the transformative potential of AI and addresses the three fundamental questions that frequently arise when discussing AI and engineering excellence:

1. How accurate can AI models be compared to a first principle simulation?
2. How Many Datasets Are Needed to Train an AI Model?
3. How Trustworthy Are AI Prediction Results?

Watch the on-demand webinar to get all your questions answered!

The Secrets of Turbulent Separation Bubbles: A PINN-based Analysis

Exploring the Power of Physics-Informed Neural Networks in Assimilating Experimental Data

The authors from the Chair of Aerodynamics at TU Berlin tackle challenges such as measurement uncertainties and the inability to capture near-wall velocity data, demonstrating that Physics-Informed Neural Networks (PINNs) effectively handle these issues. By minimizing residuals of the three-dimensional incompressible Reynolds-averaged Navier–Stokes equations, the authors incorporate underlying physics, rectifying measurement errors and predicting near-wall velocity profiles reliably. The incorporation of wall shear-stress data into the PINN training, a novel approach presented in this study, further enhances the accuracy of predictions.

Once their scalability is established for real-world engineering challenges, PINNs will offer a robust solution, especially when dealing with poorly distributed samples or the need for extrapolation beyond the range of available data. By integrating physical laws, PINNs ensure that predictions adhere to established principles, providing reliable results even in data-scarce regions. This intrinsic understanding of underlying physics makes PINNs adept at handling irregular data distributions and extrapolating intelligently, contrasting with purely data-driven models that might struggle or yield non-physical results under these conditions. Hence, PINNs emerge as a compelling choice for accurate, physics-consistent predictions across diverse engineering domains.

Read the full publication: https://pubs.aip.org/aip/pof/article/36/1/015131/3037468/Assimilating-experimental-data-of-a-mean-three

Aerodynamic Results – From Hours to Seconds

Artificial Intelligence is changing the way we engineers solve complex problems. Using Machine Learning, computers can make predictions and decisions based on test or simulation data.

What's your first thought when it comes to combining Computational Fluid Dynamics (CFD) with AI??

Do you have a use case that could benefit from this technology??

Reach out to us: https://www.navasto.de/contact/

The Need for AI in Engineering

The need for AI in engineering stems from the continuously evolving challenges faced by today’s engineering organizations.?

These include:

1. Optimization Needs: Engineers constantly seek to reduce manufacturing costs, energy consumption, material usage, and environmental impact.

1. Generative Design: The emergence of tools capable of generating thousands of design models quickly necessitates an engineering solution for evaluating and optimizing these model designs effectively.

1. Diverse Design Criteria: Engineering criteria have expanded to include factors like thermal management, vibrations, environmental and light exposure, electromagnetics, and more. Sometimes, there are multiple design objectives and standards that need to be met that cannot be easily managed using traditional methods.

1. Digital Twins: The development of digital replicas of real-world products for real-time monitoring, optimization, and anomaly detection is now a reality, demanding specialized tools.?

1. Time to market: In today’s competitive landscape, engineering organizations are under increasing pressure to accelerate the time it takes to bring new products to market. This urgency arises from the dynamic interplay of evolving technology, stringent regulations, and new market entrants.

?? Learn more about how AI helps solve these challenges: https://www.navasto.de/resources/blog/real-time-physics-predictions/?

Real-Time CFD Prediction in Blender

By leveraging a specialized plugin for Blender along with NAVASTO's machine learning API, users gain the ability to dynamically adjust the vehicle's shape.?

This unique functionality empowers users to receive instant predictions regarding the performance and flow physics of the modified design. In essence, this enables designers to make well-informed decisions during the styling process.?

The synergy between simulation results, machine learning, and Blender's design environment contributes to a seamless and efficient workflow, enhancing the precision and agility of the design iteration process.

?? ?? Watch as a deep neural network was trained on the simulation results of the DrivAer car: https://www.youtube.com/watch?v=mPdHraCXZLE&t=22s?

NAVASTO's Contribution to Engineering Innovation

Our AI engineering solutions have transformed industries, from packaging to automotive.?

Here are a few cases:

?? Digimind partnered with NAVASTO to utilize their AI-powered API for sustainable packaging transformations.

??? 通用汽车 embraced NAVASTO's AI-accelerated software package, enhancing their engineering workflows with real-time CFD and interactive design capabilities.?

?? 奥迪 NAVASTO's AI design solution speeds up automotive design and exploration.

Discover more about these partnerships and other reference projects at https://www.navasto.de/customers/#reference-projects? ?

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The future is now.

Matthias