A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
Eighty years of the finite element method: Birth, evolution, and future
This document presents comprehensive historical accounts on the developments of finite
element methods (FEM) since 1941, with a specific emphasis on developments related to …
element methods (FEM) since 1941, with a specific emphasis on developments related to …
Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network
The velocities measured by particle image velocimetry (PIV) and particle tracking
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …
Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network
(HiDeNN) is proposed to solve challenging computational science and engineering …
(HiDeNN) is proposed to solve challenging computational science and engineering …
Developments in image processing using deep learning and reinforcement learning
The growth in the volume of data generated, consumed, and stored, which is estimated to
exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for …
exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for …
A physics-informed neural network approach to solution and identification of biharmonic equations of elasticity
M Vahab, E Haghighat, M Khaleghi… - Journal of Engineering …, 2022 - ascelibrary.org
We explore an application of the Physics-Informed Neural Networks (PINNs) in conjunction
with Airy stress functions and Fourier series to find optimal solutions to a few reference …
with Airy stress functions and Fourier series to find optimal solutions to a few reference …
[HTML][HTML] Image-based modelling for adolescent idiopathic scoliosis: mechanistic machine learning analysis and prediction
Scoliosis, an abnormal curvature of the human spinal column, is characterized by a lateral
deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic …
deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic …
Convolution hierarchical deep-learning neural network tensor decomposition (C-HiDeNN-TD) for high-resolution topology optimization
High-resolution structural topology optimization is extremely challenging due to a large
number of degrees of freedom (DoFs). In this work, a Convolution-Hierarchical Deep …
number of degrees of freedom (DoFs). In this work, a Convolution-Hierarchical Deep …
Convolution hierarchical deep-learning neural network (c-hidenn) with graphics processing unit (gpu) acceleration
Abstract We propose the Convolution Hierarchical Deep-learning Neural Network (C-
HiDeNN) that can be tuned to have superior accuracy, higher smoothness, and faster …
HiDeNN) that can be tuned to have superior accuracy, higher smoothness, and faster …
Machine learning in network slicing—a survey
5G and beyond networks are expected to support a wide range of services, with highly
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …