Multifidelity deep learning modeling of spatiotemporal lung mechanics.

ABSTRACT:

Introduction

Digital twins of the respiratory system have shown promise in predicting the patient-specific response of lungs connected to mechanical ventilation. However, modeling the spatiotemporal response of the lung tissue through high-fidelity numerical simulations involves computing times that largely exceed those required in clinical applications. In this work, we present a multi-fidelity deep learning surrogate model to efficiently and accurately predict the poromechanical fields that arise in lungs connected to mechanical ventilation.

Methods

We generate training datasets with two fidelity levels from non-linear finite-element simulations on coarse (low-fidelity) and fine (high-fidelity) discretizations of the lungs domain. Further, we reduce the output spatiotempor

SUBMITTER: Barahona Yanez J 

PROVIDER: S-EPMC12504473 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature