Fossil 2.0: Formal Certificate Synthesis for the Verification and Control of Dynamical Models
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| Format: | Preprint |
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2023
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| _version_ | 1866929314799288320 |
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| author | Edwards, Alec Peruffo, Andrea Abate, Alessandro |
| author_facet | Edwards, Alec Peruffo, Andrea Abate, Alessandro |
| contents | This paper presents Fossil 2.0, a new major release of a software tool for the synthesis of certificates (e.g., Lyapunov and barrier functions) for dynamical systems modelled as ordinary differential and difference equations. Fossil 2.0 is much improved from its original release, including new interfaces, a significantly expanded certificate portfolio, controller synthesis and enhanced extensibility. We present these new features as part of this tool paper. Fossil implements a counterexample-guided inductive synthesis (CEGIS) loop ensuring the soundness of the method. Our tool uses neural networks as templates to generate candidate functions, which are then formally proven by an SMT solver acting as an assertion verifier. Improvements with respect to the first release include a wider range of certificates, synthesis of control laws, and support for discrete-time models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_09793 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fossil 2.0: Formal Certificate Synthesis for the Verification and Control of Dynamical Models Edwards, Alec Peruffo, Andrea Abate, Alessandro Systems and Control Machine Learning Logic in Computer Science This paper presents Fossil 2.0, a new major release of a software tool for the synthesis of certificates (e.g., Lyapunov and barrier functions) for dynamical systems modelled as ordinary differential and difference equations. Fossil 2.0 is much improved from its original release, including new interfaces, a significantly expanded certificate portfolio, controller synthesis and enhanced extensibility. We present these new features as part of this tool paper. Fossil implements a counterexample-guided inductive synthesis (CEGIS) loop ensuring the soundness of the method. Our tool uses neural networks as templates to generate candidate functions, which are then formally proven by an SMT solver acting as an assertion verifier. Improvements with respect to the first release include a wider range of certificates, synthesis of control laws, and support for discrete-time models. |
| title | Fossil 2.0: Formal Certificate Synthesis for the Verification and Control of Dynamical Models |
| topic | Systems and Control Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2311.09793 |