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HEIndividual fellowship2022–2024

UN-BIASED · UNcertainty quantification and modelling Bias Inhibition by means of an Agnostic Synergistic Exploitation of multi-fidelity Data

Horizon Europe — Marie Skłodowska-Curie Actions

Duration
2022-10-04 → 2024-10-03
EU contribution
€172,750
Participants
1
Scheme
HORIZON-TMA-MSCA-PF-EF

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Results in brief

UNcertainty quantification and modelling Bias Inhibition by means of an Agnostic Synergistic Exploitation of multi-fidelity Data

These are the brightest times of the data-science era, continuously striking priceless technological advancements towards the betterment of Society. Jim Gray, winner of the prestigious Turing award, recognised data science as the fourth paradigm of Science, together with experiments, theory and computation. Interestingly, Scientific Modelling is still a heavily hypotheses-driven process, strongly biased by the subjective thinking of the human mind. The recent outburst of Data Science techniques opens the path to innovative modelling paradigms. Despite the complexity of the phenomena under investigation, data-driven regression procedures seek an unbiased implicit approach to our learning experience, based on raw data from actual observations. The proposed action aims at developing an innovative Scientific Modelling (SM) paradigm closely entwining data-driven (DD) and hypotheses-driven (HD) techniques to potentially reduce, if not correct, possible cognitive biases concerning the Modeller’s subjective understanding of reality. The goal is to demonstrate the proposed methodology on a complex application of great interest to the aerospace industry, namely, tilt-rotors and multi-rotor machines. These are at the cutting edge of the modern aeronautic industry. Their unique capability of combining vertical take-off and landing with a high cruise speed, comfort, and range, makes them very attractive to the short-haul regional market, with a particular reference to electric Urban Air Mobility (UAM), search and rescue, emergency medical services and service to isolated areas. Several multi-rotor configurations are presently developed for air-taxi applications. Despite the large amount of resources pledged by the industry, many challenges remain unanswered. In particular, performance predictions are hampered by the complexity of the aerodynamics of diverse flight configurations, e.g., hover and vertical-to-horizontal flight transition. In this context, the industry calls for revolutionary modelling and design paradigms to improve the performance of multi-rotor machines. This action aims to accommodate this need by crafting an agnostic multi-fidelity modelling framework establishing a synergy between the theory-to-data and the data-to-theory perspectives to identify and possibly mitigate epistemic uncertainty in experimental and computational models for tilt-rotors aerodynamics.

Data: CORDIS, © European Union

Project objective

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.

Original text from CORDIS.

Participants

  • POLITECNICO DI MILANO · MilanoCoordinatorItaly

Links

Data: CORDIS, © European Union