SOURCE-LINKED INTELLIGENCE

AI-MULTISCALE INTEGRATION FOR WASTE-TO-VALUE DIGITAL TWINS

CORDIS · observation · Publication date unknown

t allow first-principles detailed mechanistic information to be employed by higher time and length models . The objective of our project, AIM (Area 2 of the Challenge), is to employ the most advanced Artificial Intelligence technologies to provide the links at the multiscale that will allow deriving a Digital Twin of the device. Inorder to achieve this objective, simulations need to be pushed forward in representativity (number of configurations, reactions, models of materials) but also in capacities, understanding the activation of the materials via external stimuli such as excitations, to improve accuracy. Thus, method development will be required at different scales, including technological developments for the automatization of the workflows. All the information will be employed to achieve multiscale integration with a high degree of fidelity. We will use Artificial Intelligence tools to compress the derived information to develop the Digital Twin. Digital Twin; Workflows, Atomistic Modeling; Advanced computational methods; Multiscale; Machine Learning Models for Catalysis; Meth

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recordType

award

status

SIGNED

region

EU

value

3999508.01

unit

EUR