Formal verification of unknown dynamical systems via Gaussian process regression

J Skovbekk, L Laurenti, E Frew… - arxiv preprint arxiv …, 2021 - arxiv.org
Leveraging autonomous systems in safety-critical scenarios requires verifying their
behaviors in the presence of uncertainties and black-box components that influence the …

Contour method with uncertainty quantification: a robust and optimised framework via gaussian process regression

A Tognan, L Laurenti, E Salvati - Experimental Mechanics, 2022 - Springer
Background Over the past 20 years, the Contour Method (CM) has been extensively
implemented to evaluate residual stress at the macro scale, especially in products where …

Formal Verification of Unknown Dynamical Systems Via Gaussian Process Regression

J Skovbekk, L Laurenti, E Frew… - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Leveraging autonomous systems in safety-critical scenarios requires verifying their
behaviors in the presence of uncertainties and black-box components that influence the …

Supervised Machine Learning Approaches for Structural Integrity: Residual Stress Evaluation and Defect-based Fatigue Modelling

A Tognan - 2024 - tesidottorato.depositolegale.it
Assessing the performance of structural materials is imperative to guarantee the integrity,
service continuity, durability and load-bearing capacity of engineered parts. Amongst …

[PDF][PDF] コントローラメイクスパン最小化フレームワークの適用範囲分割と段階的比較手法

清水優希 - 2024 - waseda.repo.nii.ac.jp
コントローラメイクスパン最小化フレームワー クの適用範囲分割と段階的比較手法 Page 1 2023年度
修士論文 コントローラメイクスパン最小化フレームワー クの適用範囲分割と段階的比較手法 2024 …

[PDF][PDF] Towards Efficient Data Refinement for Data-driven Abstraction and Verification

J Skovbekk, L Laurenti, E Frew, M Lahijanian - aair-lab.github.io
Recent advances in Artificial Intelligence (AI) have propelled its integration into autonomous
systems (Winfield et al. 2019). However, applying AI to safety-critical systems poses a …