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• Piterskaya, Anna & Mortensen, Mikael (2025). A study of the Orr–Sommerfeld and induction equations by Galerkin and Petrov–Galerkin spectral methods utilizing Chebyshev polynomials. Journal of Computational and Applied Mathematics. ISSN 0377-0427. 459. doi: 10.1016/j.cam.2024.116374. Full text in Research Archive
• Piterskaya, Anna; Miloch, Wojciech Jacek & Mortensen, Mikael (2023). A global spectral-Galerkin investigation of a Rayleigh-Taylor instability in plasma using an MHD-Boussinesq model. AIP Advances. 13(10). doi: 10.1063/5.0155976. Full text in Research Archive Show summary

  This paper presents a new efficient algorithm based on the spectral-Galerkin numerical approximations complemented by a magnetohydrodynamics–Boussinesq model and a new solver for studying the development of a Rayleigh–Taylor (RT) instability. We use the Shenfun computational framework in the Cartesian coordinates, which gives the spectral order and accuracy for the considered model based on the magnetohydrodynamics equations and the Boussinesq conjecture. The numerical simulations were conducted for each two- and three-dimensional case, both with and without an external static magnetic field. The validity of the numerical results was examined by comparing the calculated squared L2-norm of the density parameter with the linear stability analysis. We also examined the effects of a uniform tangential magnetic field on the onset and growth of an RT instability at different magnetic field strengths. The analysis of the effectiveness of the presented method suggests that it can be modified for further research on two-component plasma.

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• Kielland, Anders; Piterskaya, Anna & Weber, Christian (2024). Machine learning advancements for vehicle safety systems - Review of technical foundations and applications. Transportøkonomisk institutt . ISSN 9788248023401. Full text in Research Archive Show summary

  This report summarizes the integration of Machine Learning (ML) in modern vehicle safety applications. Advances in ML have transformed vehicle safety, shifting from traditional rule-based systems to data-driven, adaptive technologies. These applications include advanced driver assistance, predictive maintenance, real-time traffic management, and autonomous driving. ML encompasses a broad spectrum of methodologies and offers flexibility for various implementations, enabling customization for a wide range of tasks. However, while modern ML approaches easily adapt to diversity in data, they also require substantial amounts of data to perform effectively. Furthermore, ML introduces several challenges, particularly the “black box” problem, which raises ethical and regulatory concerns, as well as issues related to privacy and cybersecurity. Addressing these challenges requires research to improve transparency of models, fairness, and trust in ML-driven safety systems. Importantly, the growing availability of vehicle- and traffic-generated data, enabled by Vehicle-to-Everything communication and smart city infrastructure, further highlights ML’s potential for enhancing vehicle safety.

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