3 papers
physics.comp-ph2026
PI-GINOT: Data-free geometry-informed neural operator learning for finite-strain hyperelasticity on parametric DogBone specimens
Aamir Dean, Betim Bahtiri
Parametric nonlinear solid-mechanics simulations are widely used in virtual testing, optimisation, and uncertainty analysis, but repeated finite-element simulations become costly w…
physics.comp-ph2026
A Puck-informed mode-resolved phase-field fatigue framework for unidirectional composites
Aamir Dean
Fatigue fracture in unidirectional fibre-reinforced composites is strongly mode dependent: transverse and off-axis cycling is governed by matrix and inter-fibre mechanisms, whereas…
physics.comp-ph2025
A hybrid electromechanical phase-field and deep learning framework for predicting fracture in dielectric nanocomposites
Aamir Dean, Jaykumar Mavani, Betim Bahtiri +2
The accurate and efficient prediction of crack propagation in dielectric materials is a critical challenge in structural health monitoring and the design of smart systems. This wor…