Upcoming Webinar: AI Accelerated Engineering: Real-Time Results for Real-World Engineering Challenges
Join us for this exclusive webinar on December 12th at 16:00 CET to explore real-time, data-driven solutions for complex engineering challenges. Discover successful AI implementations in engineering, covering AI models and real-time examples, and learn about the applications of transfer learning.
NAVASTO News: New Innovation Partnership With INEOS Britannia
The NAVASTO team has announced an innovation partnership with INEOS Britannia, the famous British sailing team challenging to win the 37th America’s Cup.
Webinar Collaboration: AI-Based Physics Predictions in Your Web-Browser With SimScale
During our collaborative webinar, SimScale showcased their AI-powered physics simulations, which have the potential to revolutionize engineering. They aim to make simulation accessible to every engineer by offering AI tools alongside the physics solvers, all accessible through a simple login on a web browser.
Webinar: Next-Generation Tools for Automotive Aerodynamic Design and Optimization
In this webinar, we explore the application of Artificial Intelligence (AI) in engineering workflows, specifically focusing on aerodynamics and optimizing vehicle performance.
Webinar: Boosting Marine CFD with AI
Upgrade your browsing with AI-generated physics predictions for real-time interactivity.
Blog: Real-Time Physics Predictions Empowered by AI
Upgrade your browsing with AI-generated physics predictions for real-time interactivity.
Blog: Enhancing Aerodynamics With Artificial Intelligence: A Revolution in Engineering
Learn how NAVASTO’s AI technology is revolutionizing product development & EV aerodynamics. Discover the power of real-time insights for engineering.
Blog: The Future of Maritime Travel: The Next Phase in Ship Design Evolution
Ship designers face challenges like emissions regulations, efficiency optimization, and future-proof propulsion. AI advancements enable real-time prediction of flow fields and scalar quantities, accelerating product development. A pilot boat project used parametric models and simulations with CAESES and Simcenter STAR-CCM+. Machine learning models based on geometric parameters facilitated rapid design processes. This blog discusses the project, including the creation and accuracy of the Reduced-Order Model (ROM).
Blog: Probabilistic Gaussian Processes – An Approach To Stable Response Surface Modelling for Noisy Processes
Response surface models based on Gaussian Kernels, such as Kriging, require the estimation of hyperparameters to fit the model to the training data. Commonly, this estimation is made by exploiting the maximum marginal likelihood (MML) function. However, this is not an easy optimization problem due to the topology of the MML function. An alternative – and more robust – approach to hyperparameter estimation can be based on Bayesian statistics.
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