Objective
Forests, insect swarms, bones during remodeling, are striking examples of biological systems whose elements possess the ability to sense and exchange signals. These signals are exploited to adapt to evolving environmental conditions and to learn how to improve performance, in some cases without a centralized control.
Can materials and structures be enabled with the same capabilities? How can we build devices that exchange information on a mechanistic base and exploit these to learn how to optimally react to external stimuli? To what extent can materials and structures be endowed with active inference processes which mimic brain activities?
Finding answers to these questions is the challenge of IMMENSE, with the overarching aim to create materials and structures able to sense, exchange signals, interpret and compare them, thus achieving self-learning and self-adaptation. This will be a major step toward the design of sentient materials and structures.
Solid and structural mechanics, solid-fluid interaction, smart architected metamaterials, coupled with multi-physics phenomena at micro and macro scales, will be combined to implement sensing and signal control abilities on mechanistic bases.
Complex dynamic responses of oscillator arrays, coupled with physical “in materia” computing replicating classification and learning processes, will be innovative tools designed to implement learning and reacting abilities.
Experiments will be performed at micro and meso scales on “ad hoc” designed proof of principles prototypes, to obtain evidence of sentient materials and structures.
IMMENSE will set the stage for a new class of materials and structures implemented with local, decentralized sensing, monitoring and reacting abilities and will open to a variety of new applications, including local self-healing of construction materials and biomedical prosthesis, new monitoring and control of industrial appliances, advanced unmanned vehicles and satellites.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- natural sciencesbiological scienceszoologyentomology
- natural sciencescomputer and information sciencescomputational sciencemultiphysics
- medical and health sciencesmedical biotechnologyimplants
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
20133 Milano
Italy