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NAVASTO Blogs

How to Optimize Your 3D Workflows With Advanced Geometry Modification and Visualization

NAVASTO’s AI-driven solutions transform engineering workflows, accelerating development cycles, reducing costs, and empowering innovation. From real-time CFD predictions to streamlined design workflows, discover how advanced geometry modification and visualization can optimize your 3D workflows for enhanced productivity and efficiency.

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5 Complex Engineering Challenges Solved with NAVASTO's AI Solutions

In today’s world, the boundaries of engineering are constantly redefined by the power of Artificial Intelligence (AI). With this blog post, NAVASTO welcomes you to an era where the previously unattainable is today turned into reality.

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ai guided design navasto

The 5 Benefits of AI-Driven Design in Engineering

AI can open up new opportunities for innovation and problem-solving that were previously unattainable, leading to an expansion of the engineering field rather than its contraction.

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INEOS Britannia Partnership Announcement

Putting the AI in Sailing: NAVASTO and INEOS Britannia Join Forces for America's Cup 2024

We are thrilled to announce an exciting innovation partnership between NAVASTO and INEOS Britannia, the formidable British sailing team challenging for victory in the 37th America’s Cup.

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Real-Time Physics Predictions Empowered by AI

Real-Time Physics Predictions Empowered by AI

Upgrade your browsing with AI-generated physics predictions for real-time interactivity.

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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.

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Probabilistic Gaussian Processes

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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navasto ai engineering Ship Design Evolution

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).

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