Advancing Machine Learning-Enhanced Flow Modeling for Collision Phenomena in Total Cavopulmonary Connection
收藏DataCite Commons2026-05-03 更新2026-05-04 收录
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https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/RA94KL
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资源简介：
This study underscores the potential of ML-enabled models to enhance the efficiency of hemodynamic assessments in TCPC with flow collision scenarios. Given that flow collision phenomena are common in various physiological systems and engineering contexts, these findings may drive advancements in ML-augmented flow modeling across a broad range of applications.
提供机构：
Harvard Dataverse
创建时间：
2026-05-03
