about
A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.A new inverse method for estimation of in vivo mechanical properties of the aortic wall.A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images.Estimation of in vivo mechanical properties of the aortic wall: A multi-resolution direct search approach.Biobased High-Performance Rotary Micromotors for Individually Reconfigurable Micromachine Arrays and Microfluidic Applications.A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis.A machine learning approach as a surrogate of finite element analysis-based inverse method to estimate the zero-pressure geometry of human thoracic aortaIdentification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scansEstimation of in vivo constitutive parameters of the aortic wall using a machine learning approachLetter to the editor regarding the paper titled "on the role of material properties in ascending thoracic aortic aneurysms"
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Q38733783-FFDAAC8E-00AF-4F6A-A4B8-1EAA2DF3C302Q38791885-B68FD4A9-E7CF-4EB9-A4BC-CA75B169C900Q45945516-C468F4BF-D5C3-4FDF-9B77-E54061D88DFEQ47381997-AD8E75C7-E63D-4A32-BEFE-A3AC32793F07Q48109099-631FA9BB-E403-4B72-851B-AE0EA4C20561Q48172745-A283D3EC-D1C1-451E-A2FE-DC8F43545945Q88611741-EC476731-8D99-4521-95A2-B5082E865251Q90045134-7AEA4715-5F1F-4D89-B551-89DCC4C536F7Q92503703-542CE364-4327-4014-AFE9-8DE2793ACF9BQ92526199-4AC39BB2-4DCA-4BC7-A12B-9717975AD8F7
P50
description
investigador
@es
researcher
@en
wetenschapper
@nl
name
Minliang Liu
@en
Minliang Liu
@nl
type
label
Minliang Liu
@en
Minliang Liu
@nl
prefLabel
Minliang Liu
@en
Minliang Liu
@nl
P31
P496
0000-0001-6240-5116