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UN-BIASED

Financiado

UNcertainty quantification and modelling Bias Inhibition by means of an Agnostic...

UNcertainty quantification and modelling Bias Inhibition by means of an Agnostic Synergistic Exploitation of multi-fidelity Data The UN-BIASED project aims at developing an innovative Scientific Modelling paradigm capable of mitigating potential cognitive biases affecting the modelling process in engineering applications. Nowadays, modelling is mostly a sub... ver más

03/10/2024

POLIMI

173K€

Presupuesto del proyecto: 173K€

Líder del proyecto

POLITECNICO DI MILANO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-10-03

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

I+D

Cerrada hace 3 años

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1 Participantes

POLIMI

172.75K€ | Lider

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Duración del proyecto: 27 meses Fecha Inicio: 2022-06-13
Fecha Fin: 2024-10-03

Líder del proyecto

POLITECNICO DI MILANO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 173K€

Descripción del proyecto The UN-BIASED project aims at developing an innovative Scientific Modelling paradigm capable of mitigating potential cognitive biases affecting the modelling process in engineering applications. Nowadays, modelling is mostly a subjective process, strongly driven by the prejudice of the Modeller and anchored to the knowledge of well-determined pre-set physics. In practical applications, this often results into models affected by epistemic uncertainty. Data-driven techniques open the path for the construction of computerized models that are able to learn the physics underlying a complex system from the available data alone, requiring little, if not at all, subjectivity. Interestingly, these tools are generally used to obtain mere predictions and no credit is usually given to the possibility of translating the learned patterns and relations into interpretable theories and hypotheses. I propose to assess the physics learned by data-driven algorithms in terms of compliance with fundamental principles e.g., laws of thermodynamics, and to test them against a priori subjective hypotheses. This will expose differences between the actual experiment and the Modeller’s understanding of it. This allows for inverting the rationale underlying the classical modelling process, from a theory-to-data deductive assessment to a data-to-theory inductive inference. The ultimate goal is to advance the state-of-the-art by crafting a two-way modelling framework combining the hypotheses-driven and the data-driven approaches, to mitigate the consequences of biased modelling choices and improve the knowledge about complex physical systems. The proposed paradigm is not to be intended as a substitution of the classical Scientific Modelling method, but rather as an extension of it. The project is conceived with aerospace applications in mind, but the proposed methodology is straightforwardly applicable to the modelling of any physical problem of interest for the academy or the industry.

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