A Review of Equation-Based and Data-Driven Reduced Order Models featuring a Hybrid cardiovascular application
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911220998602752 |
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| author | Siena, Pierfrancesco Africa, Pasquale Claudio Girfoglio, Michele Rozza, Gianluigi |
| author_facet | Siena, Pierfrancesco Africa, Pasquale Claudio Girfoglio, Michele Rozza, Gianluigi |
| contents | Cardiovascular diseases are a leading cause of death in the world, driving the development of patient-specific and benchmark models for blood flow analysis. This chapter provides a theoretical overview of the main categories of Reduced Order Models (ROMs), focusing on both projection-based and data-driven approaches within a classical setup. We then present a hybrid ROM tailored for simulating blood flow in a patient-specific aortic geometry. The proposed methodology integrates projection-based techniques with neural network-enhanced data-driven components, incorporating a lifting function strategy to enforce physiologically realistic outflow pressure conditions. This hybrid methodology enables a substantial reduction in computational cost while mantaining high fidelity in reconstructing both velocity and pressure fields. We compare the full- and reduced-order solutions in details and critically assess the advantages and limitations of ROMs in patient-specific cardiovascular modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Review of Equation-Based and Data-Driven Reduced Order Models featuring a Hybrid cardiovascular application Siena, Pierfrancesco Africa, Pasquale Claudio Girfoglio, Michele Rozza, Gianluigi Numerical Analysis Medical Physics Cardiovascular diseases are a leading cause of death in the world, driving the development of patient-specific and benchmark models for blood flow analysis. This chapter provides a theoretical overview of the main categories of Reduced Order Models (ROMs), focusing on both projection-based and data-driven approaches within a classical setup. We then present a hybrid ROM tailored for simulating blood flow in a patient-specific aortic geometry. The proposed methodology integrates projection-based techniques with neural network-enhanced data-driven components, incorporating a lifting function strategy to enforce physiologically realistic outflow pressure conditions. This hybrid methodology enables a substantial reduction in computational cost while mantaining high fidelity in reconstructing both velocity and pressure fields. We compare the full- and reduced-order solutions in details and critically assess the advantages and limitations of ROMs in patient-specific cardiovascular modeling. |
| title | A Review of Equation-Based and Data-Driven Reduced Order Models featuring a Hybrid cardiovascular application |
| topic | Numerical Analysis Medical Physics |
| url | https://arxiv.org/abs/2510.17331 |