Continuous Assurance of Digital Assets via Conformal Prediction over Polytope-Defined Operating Regions

Hee Jong Park, Kristoffer Skare, Cesar Augusto Ramos de Carvalho, Claas Rostock
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:844-866, 2026.

Abstract

Continuous assurance is a process for applying an agile, modular and iterative process to re-assure and verify changes in digital assets, such as simulation models, against a set of predefined requirements. In the recent years, several verification methods have been developed that can be used in the continuous assurance context to achieve statistically sufficient coverage of simulation space. Instead of exhaustively traversing the simulation search space to find a solution that falsifies the requirements, these methods partition the space into smaller and interpretable sub-regions. Statistical methods for computing prediction intervals for the subsequent sample provides a basis for determining whether simulation parameter sampled from these sub-regions are likely to satisfy or violate the requirement contract. In this work, we apply such techniques to test a converter controller model used in a grid-forming wind turbine, within the context of a continuous assurance cycle. Additionally, we extend the search space partitioning method from existing literature by creating regions of convex polytopes near class boundaries. These regions are linear in the optimization problem, with the aim to reduce the regions of uncertain areas during classification. We further demonstrate the partitioning method with an additional set of benchmarks including the mountain car and F-16 GCAS simulation model.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-park26a, title = {Continuous Assurance of Digital Assets via Conformal Prediction over Polytope-Defined Operating Regions}, author = {Park, Hee Jong and Skare, Kristoffer and Ramos de Carvalho, Cesar Augusto and Rostock, Claas}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {844--866}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/park26a/park26a.pdf}, url = {https://proceedings.mlr.press/v329/park26a.html}, abstract = {Continuous assurance is a process for applying an agile, modular and iterative process to re-assure and verify changes in digital assets, such as simulation models, against a set of predefined requirements. In the recent years, several verification methods have been developed that can be used in the continuous assurance context to achieve statistically sufficient coverage of simulation space. Instead of exhaustively traversing the simulation search space to find a solution that falsifies the requirements, these methods partition the space into smaller and interpretable sub-regions. Statistical methods for computing prediction intervals for the subsequent sample provides a basis for determining whether simulation parameter sampled from these sub-regions are likely to satisfy or violate the requirement contract. In this work, we apply such techniques to test a converter controller model used in a grid-forming wind turbine, within the context of a continuous assurance cycle. Additionally, we extend the search space partitioning method from existing literature by creating regions of convex polytopes near class boundaries. These regions are linear in the optimization problem, with the aim to reduce the regions of uncertain areas during classification. We further demonstrate the partitioning method with an additional set of benchmarks including the mountain car and F-16 GCAS simulation model.} }
Endnote
%0 Conference Paper %T Continuous Assurance of Digital Assets via Conformal Prediction over Polytope-Defined Operating Regions %A Hee Jong Park %A Kristoffer Skare %A Cesar Augusto Ramos de Carvalho %A Claas Rostock %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-park26a %I PMLR %P 844--866 %U https://proceedings.mlr.press/v329/park26a.html %V 329 %X Continuous assurance is a process for applying an agile, modular and iterative process to re-assure and verify changes in digital assets, such as simulation models, against a set of predefined requirements. In the recent years, several verification methods have been developed that can be used in the continuous assurance context to achieve statistically sufficient coverage of simulation space. Instead of exhaustively traversing the simulation search space to find a solution that falsifies the requirements, these methods partition the space into smaller and interpretable sub-regions. Statistical methods for computing prediction intervals for the subsequent sample provides a basis for determining whether simulation parameter sampled from these sub-regions are likely to satisfy or violate the requirement contract. In this work, we apply such techniques to test a converter controller model used in a grid-forming wind turbine, within the context of a continuous assurance cycle. Additionally, we extend the search space partitioning method from existing literature by creating regions of convex polytopes near class boundaries. These regions are linear in the optimization problem, with the aim to reduce the regions of uncertain areas during classification. We further demonstrate the partitioning method with an additional set of benchmarks including the mountain car and F-16 GCAS simulation model.
APA
Park, H.J., Skare, K., Ramos de Carvalho, C.A. & Rostock, C.. (2026). Continuous Assurance of Digital Assets via Conformal Prediction over Polytope-Defined Operating Regions. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:844-866 Available from https://proceedings.mlr.press/v329/park26a.html.

Related Material