Deep Multiphysics and Particle–Neuron Duality: A Computational Framework Coupling (Discrete) Multiphysics and Deep Learning

Deformation and rupture of compound cells under shear: A discrete multiphysics study

(2019) Amin Rahmat et al.   PHYSICS OF FLUIDS

Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology

(2019) Alessio Alexiadis   ARTIFICIAL INTELLIGENCE IN MEDICINE

Numerical simulation of dissolution of solid particles in fluid flow using the SPH method

(2019) Amin Rahmat et al.   INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW

Natural convection and solidification of phase-change materials in circular pipes: A SPH approach

(2018) Alessio Alexiadis et al.   COMPUTATIONAL MATERIALS SCIENCE

Using Discrete Multi-Physics for studying the dynamics of emboli in flexible venous valves

(2018) M. Ariane et al.   COMPUTERS & FLUIDS

A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis

(2018) Liang Liang et al.   Journal of the Royal Society Interface

Discrete multi-physics: A mesh-free model of blood flow in flexible biological valve including solid aggregate formation

(2017) Mostapha Ariane et al.   PLoS One

A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity

(2016) Rubén Ibañez et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

The Discrete Multi-Hybrid System for the Simulation of Solid-Liquid Flows

(2015) Alessio Alexiadis   PLoS One

A smoothed particle hydrodynamics and coarse-grained molecular dynamics hybrid technique for modelling elastic particles and breakable capsules under various flow conditions

(2014) Alessio Alexiadis   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS