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Open access Sep 2026

Hyperelastic Cardiovascular NN–FE: A Framework for Integrating Nonlinear Finite Element Solvers with Neural Networks for the Hyperelastic Modeling of Cardiac Valve Tissue

The NN–FE framework represents a computationally efficient surrogate model suitable for real-time applications in cardiovascular biomechanics, and achieved mean absolute percentage errors (MAPE) less than 15% for displacement predictions and temporal coefficients of determination exceeding 0.85 when predicting out-of-d...

Maedeh Makki, Gerhard A. Holzapfel, M. Raissi et al. · 0 citations
Open access

PINN-Sed: Interconnected Physics-Informed Machine Learning Model for High Resolution Suspended Sediment Transport in River Network

There is growing demand for river-network models that can estimate sediment transport at fine temporal resolution. Although coupling physically based sediment modules with hydrological and hydrodynamic models is a logical path forward, these approaches require intensive calibration and substantial computing power, li...

A. Haddadchi, Neshat Movahedi, Reza Akbarian-Bafghi et al. · 0 citations

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