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NeuralShape: the first deep-learning based software to improve the engineer ‘work.

Periodic Reporting for period 1 - NeuralShape (NeuralShape: the first deep-learning based software to improve the engineer ‘work.)

Reporting period: 2019-03-01 to 2019-06-30

Deep Learning (DL) algorithms have revolutionized how images, sounds and natural language are processed. Unfortunately, such techniques have only started making inroads into the domain of CAE. Neural Concept have developed NeuralShape: a patent protected AI assistant for engineers using CAD/CFD software. Our DL based SW allows to speed up R&D cycles, enhance product performance and reduce computational costs. Our tools and methods have already successfully solved several real-world problems, have beaten industry benchmarks and have shown promising results for a wide range of applications. The objective of this project is the industrial adaptation and scaling up of our innovative DL system.
During the FS we precisely designed the technical activities required to reach a TRL 8 through the optimization of our algorithms and our user interface as well as through validation activities carried out with industry leaders. A full market research was carried out identifying: target clients, potential customers, addressable market and competitors. A prior-art analysis was carried out concluding that we have Freedom-to-Operate in our target countries. Moreover, we established our commercialization strategy consisting on addressing engineering firms through direct sales and via online SaaS cloud interface. Additionally, we will rely on partners to distribute our solution as an integrated plugin within their SW platforms. We intend to scale-up our business first in Switzerland, UK, Germany and France.
NeuralShape is the first DL system that has a notion of its own body-shape. It is agnostic to the optimization parameters as it directly works with the mesh representation of the design and a single predictor can be trained with a large amount of data while becoming more powerful over time. It can also leverage historical data from different sources without requiring designs to share an underlying parametrization. The main expected impact from NeuralShape is a reduction of design development times and the selection of optimal designs under rationally defined criteria. This translates into higher accuracy, time saving and reduction of staff requirements for CAE, which, on a broader societal scale, translate into reduced energy and resource consumption from the design process and from optimally designed vehicles, aircrafts and ships.
Neural Shape can optimize a drone design automatically in a few iterations