guest ::
| 001 | 1016297 | ||
| 005 | 20250823040225.0 | ||
| 024 | 7 | _ | |2 CORDIS |a G:(EU-Grant)101141626 |d 101141626 |
| 024 | 7 | _ | |2 CORDIS |a G:(EU-Call)ERC-2023-ADG |d ERC-2023-ADG |
| 024 | 7 | _ | |2 originalID |a corda_____he::101141626 |
| 024 | 7 | _ | |2 doi |a 10.3030/101141626 |
| 035 | _ | _ | |a G:(EU-Grant)101141626 |
| 150 | _ | _ | |a Automated Model Discovery for Soft Matter Systems |y 2024-07-01 - 2029-06-30 |
| 372 | _ | _ | |a ERC-2023-ADG |s 2024-07-01 |t 2029-06-30 |
| 450 | _ | _ | |a DISCOVER |w d |y 2024-07-01 - 2029-06-30 |
| 510 | 1 | _ | |0 I:(DE-588b)5098525-5 |a European Union |b CORDIS |
| 680 | _ | _ | |a
Automated models boosting research on soft matter. Soft materials, which can be easily deformed or structurally altered by thermal or mechanical stress, are essential in modern life, affecting autonomy, sustainability and health. However, accurately modelling these materials is complex and usually limited to a few well-trained experts. The ERC-funded DISCOVER project aims to make constitutive modelling more accessible through automated model discovery. Objectives include developing neural networks that autonomously find the best models, parameters and experiments for various soft matter systems. Furthermore, researchers will assess model performance in different experiments and use Bayesian analysis to measure uncertainties. Automated model discovery should enable exploration of a vast range of model parameters, offering insight into soft matter systems that traditional methods cannot achieve.
|
| 909 | C | O | |o oai:publications.rwth-aachen.de:1016297 |p authority:GRANT |p authority |
| 909 | C | O | |o oai:publications.rwth-aachen.de:1016297 |
| 980 | _ | _ | |a G |
| 980 | _ | _ | |a AUTHORITY |
| 980 | _ | _ | |a CORDIS |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|