5 papers
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…
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…
A multiphysics deep energy method for fourth-order phase-field fracture with piezoresistive self-sensing
Aamir Dean, Betim Bahtiri
Piezoresistive materials can act as self-sensing media because deformation and cracking modify their electrical resistance. This paper presents a fracture-informed multiphysics fra…
A multi-phase-field model for fiber-reinforced composite laminates based on puck failure theory
Pavan Kumar Asur Vijaya Kumar, Rafael Fleischhacker, Aamir Dean +1
This article proposes a multi-phase-field model using the Puck failure theory to predict the failure in fiber-reinforced composites (FRCs) laminates. Specifically, this work propos…
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…