Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
projects
pyforce
A Python package for data-driven reduced-order modelling of multiphysics problems
PyVista for OpenFOAM
Example on how to use PyVista to visualize OpenFOAM results
pySHRED and NuSHRED
Python packages for Shallow Recurrent Decoders (SHRED) and a specific version for nuclear applications (NuSHRED)
publications
Stabilization of Generalized Empirical Interpolation Method (GEIM) in presence of noise: A novel approach based on Tikhonov regularization
C. Introini, S. Cavalleri, S. Lorenzi, Stefano Riva, A. Cammi ; Computer Methods in Applied Mechanics and Engineering (2023)
This paper is about a stabilisation method for Generalized Empirical Interpolation Method (GEIM) in presence of noise.
Indirect Field Reconstruction and Sensor Positioning in Circulating Fuel Reactors using Data-Driven Model Order Reduction
A. Cammi, Stefano Riva, C. Introini, L. Loi, E. Padovani. ; ICAPP 2023 - International Conference on Advances in Nuclear Power Plants (2023)
This paper presents the application of indirect reconstruction and sensor placements methods for state reconstruction in a Molten Salt Reactor.
Hybrid Data Assimilation methods, Part II: Application to the DYNASTY experimental facility
Stefano Riva, C. Introini, S. Lorenzi, A. Cammi ; Annals of Nuclear Energy (2023)
This paper investigates the applications of various hybrid data assimilation methods to the DYNASTY experimental facility.
Multi-physics model bias correction with data-driven reduced order techniques: Application to nuclear case studies
Stefano Riva, C. Introini, A. Cammi ; Applied Mathematical Modelling (2024)
This paper presents the use of TR-GEIM and PBDW for multi-physics model bias correction in nuclear applications.
PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
D. Ye and J. Williams and M. Gao and Stefano Riva and M. Tomasetto and DavD.id Zoro and J. N. Kutz ; Arxiv (2025)
This paper presents the package PySHRED for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery.
Verification and Validation of Shallow Recurrent Decoders for State Estimation in the DYNASTY facility
Stefano Riva, A. Missaglia, C. Introini, J. N. Kutz, A. Cammi, ; 21st International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-21) (2025)
This paper presents a verification and validation of Shallow Recurrent Decoders for state estimation in the DYNASTY facility.
Robust state estimation from partial out-core measurements with Shallow Recurrent Decoder for nuclear reactors
Stefano Riva, C. Introini, A. Cammi, J. Nathan Kutz ; Progress in Nuclear Energy (2025)
This paper presents the use of Shallow Recurrent Decoder for robust state estimation in nuclear reactors.
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
P. M. Wyder, J. A. Goldfeder, A. Yermakov, Y. Zhao, Stefano Riva, J. P. Williams, D. Zoro, A. S. Rude, M. Tomasetto, J. Germany, J. Bakarji, G. Maierhofer, M. Cranmer, J. N. Kutz ; The 39th Annual Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track (2025)
This paper presents a common task framework for evaluating scientific machine learning algorithms, focusing on their application to complex dynamical systems.