Antonio Cammi

Orcid: 0000-0003-1508-5935

According to our database1, Antonio Cammi authored at least 14 papers between 2004 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
pyforce-1.0.0: Python Framework for data-driven model Order Reduction of multi-physiCs problEms.
CoRR, May, 2026

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models.
CoRR, May, 2026

Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors.
CoRR, April, 2026

Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics.
CoRR, March, 2026

pyforce: Python Framework for data-driven model Order Reduction of multi-physiCs problEms.
J. Open Source Softw., January, 2026

2025
Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor.
CoRR, October, 2025

A Comparison of Parametric Dynamic Mode Decomposition Algorithms for Thermal-Hydraulics Applications.
CoRR, March, 2025

From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility.
CoRR, March, 2025

Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks.
CoRR, March, 2025

2024
Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors.
CoRR, 2024

Multi-Physics Model Bias Correction with Data-Driven Reduced Order Modelling Techniques: Application to Nuclear Case Studies.
CoRR, 2024

2021
Calculation of gamma-ray exposure buildup factor based on backpropagation neural network.
Expert Syst. Appl., 2021

2018
A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants.
Reliab. Eng. Syst. Saf., 2018

2004
Dagger-sampling variance reduction in Monte Carlo reliability analysis.
Monte Carlo Methods Appl., 2004


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