Minliang Liu

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Assistant Professor

Texas Tech University

United States

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Minliang Liu is an Assistant Professor at Texas Tech University, USA. His research focuses on biomechanics, particularly in relation to the human aorta, incorporating deep learning and finite element analysis. Recent publications include studies on patient-specific heart geometry modeling, the effects of arterial tissue properties on pulse wave velocity, and the development of computational frameworks for understanding aortic growth and stress analysis.

Recent Grants

Grant: Close

A data efficient computational framework for prediction of false lumen growth in type B aortic dissection

Open Date: 2023-01-01

Close Date: 2024-12-31

Grant: Close

Predoctoral Fellowship

Open Date: 2019-01-01

Close Date: 2020-12-01

Articles (11)

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2021

Collaborators (2)

Bulat A. Ziganshin

Yale University School of Medicine

UNITED STATES

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John Onofrey

Assistant Professor

Yale University School of Medicine

UNITED STATES

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Minliang Liu

Research Interests

Explore related searches

Contact this professor

About

Recent Grants

Articles (11)

The role of anatomic shape features in the prognosis of uncomplicated type B aortic dissection initially treated with optimal medical therapy

Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics

PyTorch-FEA: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta

Effect of Nonlinear Hyperelastic Property of Arterial Tissues on the Pulse Wave Velocity Based on the Unified-Fiber-Distribution (UFD) Model

Patient-specific Heart Geometry Modeling for Solid Biomechanics using Deep Learning

Constructing growth evolution laws of arteries via reinforcement learning

Ultimate tensile strength and biaxial stress–strain responses of aortic tissues—A clinical-engineering correlation

A novel computational growth framework for biological tissues: Application to growth of aortic root aneurysm repaired by the V-shape surgery

Engineering analysis of aortic wall stress and root dilatation in the V-shape surgery for treatment of ascending aortic aneurysms

A probabilistic and anisotropic failure metric for ascending thoracic aortic aneurysm risk assessment

Computation of a probabilistic and anisotropic failure metric on the aortic wall using a machine learning-based surrogate model

Collaborators (2)

Bulat A. Ziganshin

John Onofrey

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