arXiv (All)
open access
Accelerated Patient-Specific Hemodynamic Simulations with Hybrid Physics-Based Neural Surrogates
Physics-based 0D reduced-order models provide computationally lightweight predictions of cardiovascular flows, resolving bulk hemodynamics in fractions of a second that would take days to solve using traditional 3D finite-element techniques. However, the accuracy of 0D models is limited as a result
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation#1034834
- Accelerated Patient-Specific Hemodynamic Simulations with Hybrid Physics-Based Neural Surrogates#789618
- Cardiac Mechanics Modeling: Recent Developments and Current Challenges#485373
- Cardiac mechanics modeling: recent developments and current challenges#153131
- Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm#1084988
Record · ID 793622
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive