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However, the mechanical properties of airway tissues remain poorly characterized, leading to a mismatch between stent mechanical behavior and airway compliance, which can reduce stent biocompatibility and clinical effectiveness. Such mismatches can result in abnormal stress transfer at the stent\u2013airway interface and cause stent migration, local tissue irritation or inflammation, and impaired long-term performance. Predictive models are often used in treatment planning, however, without a comprehensive understanding of airway properties, advancements in modeling remain highly limited. In this study, we develop an experimental to numerical pipeline for identifying the mechanical properties of the human airway branching network, specifically the trachea, right bronchus, and left bronchus tissues. Biaxial planar tensile tests are used to capture the tissues\u2019 unique, anisotropic mechanical response in order to inform the computational routine\u2014where inverse finite element analysis is employed to calibrate a Holzapfel\u2013Gasser\u2013Ogden constitutive model. To overcome the computational burden of traditional IFEA, a neural network (NN) surrogate model is trained to enable rapid material identification. We identify parameter values for the various tissues, such as\n                    <jats:italic>C<\/jats:italic>\n                    <jats:sub>10<\/jats:sub>\n                    \u2009=\u20090.53\u2009\u00b1\u20090.25\u00a0kPa,\n                    <jats:italic>k<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    \u2009=\u20090.17\u2009\u00b1\u20090.30\u00a0kPa,\n                    <jats:italic>k<\/jats:italic>\n                    <jats:sub>2<\/jats:sub>\n                    \u2009=\u20096.1\u2009\u00b1\u20092.0, and\n                    <jats:italic>\u03ba<\/jats:italic>\n                    \u2009=\u20090.08\u2009\u00b1\u20090.01 for the trachea. Notably, this NN-approach reduced computational time from weeks to mere minutes, enabling fast material characterization. Thus, we also provide an efficient modeling framework and user-friendly MATLAB application for future studies. Ultimately, the findings here critically contribute to our current understanding of airway biomechanics and provide essential data for accurate modeling and optimization of airway stenting strategies.\n                  <\/jats:p>","DOI":"10.1007\/s10237-026-02081-7","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T07:40:45Z","timestamp":1780472445000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human airway material characterization via inverse finite element analysis and neural network surrogate"],"prefix":"10.1007","volume":"25","author":[{"given":"Arif","family":"Badrou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Crystal A.","family":"Mariano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talyah M.","family":"Nelson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona","family":"Eskandari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"issue":"13","key":"2081_CR1","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1016\/j.jbiomech.2011.06.009","volume":"44","author":"R Amelon","year":"2011","unstructured":"Amelon R, Cao K, Ding K, Christensen GE, Reinhardt JM, Raghavan ML (2011) Three-dimensional characterization of regional lung deformation. 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