Resources

Mubea is an international partner to the automotive industry and an innovative lightweight specialist. In this paper we focus on showing how using NC Shape allowed Mubea to predict 3D-field responses for a given data set, envisage a design verification tool potentially in the hands of designers, and capitalize on its own accumulated knowledge and experience, in terms of stored data.

Neural Concept's latest collaboration with Ansys and Optimo Medical enables Optimeyes to provide ophthalmologists with patient specific surgery plans within a few minutes where hours were needed with previous versions.

Numerous factors affect the number of simulations you need to train your AI model for engineering design cycles. In this article, we show some insights we have gathered from our experience over the years.

In comparison to classical AI-based models, 3D Deep Learning based predictive models use raw, 3D CAD and CAE data, and are completely agnostic to any specific parametrization of designs. This allows the same model to handle and be trained across many geometrical parametrizations. Ultimately, any simulation result, coming from any CAE team, can be re-used inside these models, making them much more flexible and thus, drastically reducing the effort needed to collect the data.

Turbomachines are very complex assemblies, which need to operate efficiently under a wide range of operating conditions. Simulation-driven design is now a key driver in the industry, but some major bottlenecks remain, which limit the improvements that are possible. How can NC Shape overcome these limitations, and radically change the design process of turbomachines? You can check our current work, in collaboration with NUMECA International, allowing for quasi-real-time performance maps and design space exploration.

Shape's predictive models and powerful multi-objective optimization features can be used to find highly innovative and performant designs. Our Korean partners and resellers from @CAD-IT used NC Shape to run a multi-objective optimization of a car's side view mirror design, on behalf of an automotive customer. Shape minimized both lift and drag forces while ensuring that the mirror's surface stays unchanged.

DEBOSH utilizes Bayesian Optimization (BO) and Graph Neural Networks (GNNs) in order to effectively explore the space of possible shapes and acquire the best performing samples according to physical properties.

Over the past few years, we have seen Neural Concept’s solution being deployed more and more within the automotive industry, by industrial leaders such as Plastic Omnium, Bosch and Stellantis. Here is what we have observed, and our thoughts on why this industry is rapidly adopting AI based design and engineering workflow.

We are pleased to have renewed a multiple-years collaboration and license agreement with Airbus.