Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
We propose a novel finite element-based physics-informed operator learning framework that
allows for predicting spatiotemporal dynamics governed by partial differential equations …
allows for predicting spatiotemporal dynamics governed by partial differential equations …
Early stage detection of scoliosis using machine learning algorithms
P Shrestha, A Singh, R Garg, I Sarraf… - … , Analytics, Big Data …, 2021 - ieeexplore.ieee.org
Scoliosis is the most common disease which is mainly identified from patient spine X-ray
images. It is mainly diagnosed based on sideways curvature image modality. In scoliosis …
images. It is mainly diagnosed based on sideways curvature image modality. In scoliosis …
Feature preserving non-rigid iterative weighted closest point and semi-curvature registration
Preserving features of a surface as characteristic local shape properties captured eg by
curvature, during non-rigid registration is always difficult where finding meaningful …
curvature, during non-rigid registration is always difficult where finding meaningful …
Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks
Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient's
initial visit is a challenging task. This work builds on our previous method and provides a …
initial visit is a challenging task. This work builds on our previous method and provides a …
Non-rigid registration via intelligent adaptive feedback control
Preserving features or local shape characteristics of a mesh using conventional non-rigid
registration methods is always difficult, as the preservation and deformation are competing …
registration methods is always difficult, as the preservation and deformation are competing …
Coupled and uncoupled dynamic mode decomposition in multi-compartmental systems with applications to epidemiological and additive manufacturing problems
Abstract Dynamic Mode Decomposition (DMD) is an unsupervised machine learning
method that has attracted considerable attention in recent years owing to its equation-free …
method that has attracted considerable attention in recent years owing to its equation-free …
Implementation and intelligent gain tuning feedback–based optimal torque control of a rotary parallel robot
Aiming at operating optimally minimizing error of tracking and designing control effort, this
study presents a novel generalizable methodology of an optimal torque control for a 6 …
study presents a novel generalizable methodology of an optimal torque control for a 6 …