A Gradient-Based Machine-Learning Inspired Inverse Modeling Approach for Characterization of Nonlinear Tissue Properties

The inverse solver is capable of recovering nonlinear material properties and fiber directions even for complex 3D problems with large deformations such as heart valves.

Authors

Institutions

Publication Details

Journal
Annals of Biomedical Engineering
Published
2026-03-04
DOI
https://doi.org/10.1007/s10439-026-04015-x
Primary Topic
Elasticity and Material Modeling
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Gradient-Based Machine-Learning Inspired Inverse Modeling Approach for Characterization of Nonlinear Tissue Properties

Iman Borazjani, Hossein Geshani
Annals of Biomedical Engineering
Elasticity and Material Modeling
article

A Gradient-Based Machine-Learning Inspired Inverse Modeling Approach for Characterization of Nonlinear Tissue Properties

Iman Borazjani, Hossein Geshani
article en

Abstract

The inverse solver is capable of recovering nonlinear material properties and fiber directions even for complex 3D problems with large deformations such as heart valves.

Annals of Biomedical Engineering
Walker (United States) (US), Texas A&M University (US)
National Science Foundation, High Performance Research Computing, Texas A and M University
Openalex Percentile: Top 37%
Elasticity and Material Modeling
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.