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Smart material data enrichment using AI, experimental data, and material science

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This webinar aims at demonstrating our approach that enables the enrichment of an initial material database by combining advanced material modelling and AI.

On the one hand, advanced material modelling, through multiscale material modelling, and embedded material science laws, bring accuracy and domain knowledge. On the other hand, AI brings efficiency, portability and quantification of the accuracy of the predictions. The database of filled and unfilled plastics is enriched for different temperatures, filling amounts, strain rates and loading angles. The targeted performances are stress strain responses until failure.

 

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Our Privacy Notice describes how we process Personal Data about you. It also provides information on how you may exercise any of your rights as a Data Subject. You have the right to withdraw your consent at any time by clicking on the "Unsubscribe" link within the electronic marketing communications sent to you. By submitting this form, you agree to our Terms of Use.

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