糖心视频

Publications

Chen Yu, Rocchetta Roberto, Nespoli Lorenzo, Medici Vasco, , ,

Proceedings of the 36th European Safety and Reliability Conference (ESREL 2026) European Safety and Reliability Conference, pp. 2949-2956 (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference, pp. 2426-2433 (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference, pp. 899-906 (2026)

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Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference (2026)

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International Journal of Disaster Risk Reduction Vol 135 (2026)

Teaching

I currently teach third year students about statically indeterminate structures, and fourth year students about the听dynamics of single and multi-degree-of-freedom systems. I have been and am currently invovled in developing lecture material and in shaping the structural analysis curriculum.

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Research Interests

* Humane algorithms for quantitative science听

* Reproducibility and open computational science

* Uncertainty quantification for green-house gas calculations听

* Open-source digital twins for structural-health monitoring

* Automated compliance checking for engineering calculations 听听

* Structural calculations with rigorous uncertainty propagation听听

* Valid and rigorous machine learning inference听

* Verification with computer arithmetics (intervals, units, automatic differentiation, probabilistic arithmetic, etc.)

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Professional Activities

Participant

15/6/2026

Advisor

15/6/2026

Peer reviewer

5/2026

Peer reviewer

1/2026

Speaker

4/12/2025

Chair

3/12/2025

Projects

Paez Jimenez, Mariana Gabriela (Post Grad Student) De Angelis, Marco (Principal Investigator) Giesekam, Jannik (Co-investigator) Shipton, Zoe (Co-investigator)

The uncertainty inherent in CO2 estimations at the organisational and product levels has received significantly less academic scrutiny than national or global inventories, yet such CO2 estimations serve as foundational data for higher-level carbon accounting. Carbon accounting often relies on practitioners whose expertise is not climate science, leading to high levels of epistemic uncertainty, methodological variation and data variability. This project addresses three critical questions: (1) How much uncertainty is inherent in these CO2 estimations? (2) Where does the uncertainty come from and how can it be mitigated? (3) How can the uncertainty be communicated to enable decisions towards net zero targets?

01-Jan-2025 - 30-Jan-2028

Tubaldi, Enrico (Principal Investigator) De Angelis, Marco (Co-investigator) Moghaddasi Kelishomi, Hamed (Co-investigator) Patelli, Edoardo (Co-investigator) Sentenac, Phillippe (Co-investigator) Tarantino, Alessandro (Co-investigator)

01-Jan-2025 - 31-Jan-2028

Tubaldi, Enrico (Principal Investigator) De Angelis, Marco (Co-investigator) Pytharouli, Stella (Co-investigator) Tarantino, Alessandro (Co-investigator)

01-Jan-2025 - 31-Jan-2027

De Angelis, Marco (Principal Investigator) McCann, Matthew (Post Grad Student)

Major global emission inventories such as EDGAR, national GHG inventories, and the IPCC tiered approach provide critical baseline data for emissions monitoring. Despite some strengths, existing emission inventories are limited by static estimates that can very quickly become outdated and by over reliance on default emission factors, which may under or overestimate actual emission within changing industrial contexts. Many of these inventories lack robust uncertainty quantification methods such as Monte Carlo simulation or error propagation. This project investigates the use of rigorous probabilistic and statistical modelling tools towards a framework for both aleatory and epistemic uncertainty quantification of CO2 estimation. State-of-the-art machine learning offers new exciting possibilities for the modelling, estimation and forecasting of CO2 emissions, providing tools that can handle complex, non-linear relationships within large datasets.

01-Jan-2023 - 31-Jan-2029

De Angelis, Marco (Principal Investigator) Loayza Romero, Karen Estefania (Co-investigator) Kazashi, Yoshihito (Academic) Ruggeri, Michele (Academic) Bi, Sifeng (Academic) Ochnio, Dawid (Post Grad Student) McIntosh, Angus (Post Grad Student)

StrathDRUMS aims to train the next generation of interdisciplinary uncertainty quantification specialists who can design and analyse state-of-the-art computational techniques and apply them to real-world challenges. An important aspect of this CDT is the non-deterministic and data-driven modelling approach, which is crucial to overcome the limitations of classical deterministic approaches. A key skill emphasised by StrathDRUMS is the uncertainty quantification, which enables the characterisation, propagation, and quantification of the inevitable uncertainties, providing model predictions over a range of outcomes (distributional, interval, fuzzy, and hybrid) instead of a unique solution with maximum fidelity to a single experiment. Providing data-driven uncertainty-aware model predictions allows for a more comprehensive understanding of the system being studied, improving the accuracy of the predictions and enabling the automatic verification of numerical simulations.

01-Jan-2023 - 31-Jan-2029