Skip to main navigation
Skip to search
Skip to main content
New Jersey Institute of Technology Home
Help & FAQ
Link opens in a new tab
Search content at New Jersey Institute of Technology
Home
Profiles
Research units
Facilities
Federal Grants
Research output
Press/Media
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla
,
Mengjia Xu
, Nathaniel Trask
, George E. Karniadakis
Research output
:
Contribution to journal
›
Review article
›
peer-review
50
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Scalable algorithms for physics-informed neural and graph networks'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Automatic Differentiation
25%
Big Data
50%
Biological Systems
25%
Data Graph
25%
Data-informed
25%
Deep Neural Network
25%
Differential Operator
25%
Engineering Problems
25%
Exterior Calculus
25%
Feedforward Neural Network
25%
Forward Problem
25%
Graph Network
100%
Graph Neural Network
75%
Hidden Physics
25%
Inverse Problem
25%
Large-scale Engineering
25%
Machine Learning
50%
Mathematical Model
25%
Multi-fidelity Data
25%
Multimodal Data
25%
Neural Network
75%
Physical Laws
25%
Physical Systems
25%
Physics Learning
25%
Physics-informed
50%
Physics-informed Machine Learning
100%
Physics-informed Neural Networks
100%
Scalable Algorithms
100%
System Data
25%
System of Systems
25%
Time-space Domain
25%
Unstructured Data
25%
Physics
Big Data
16%
Machine Learning
50%
Mathematical Model
8%
Neural Network
66%
Operators (Mathematics)
8%
Physics
100%
Chemical Engineering
Deep Neural Network
16%
Feedforward Neural Network
16%
Learning System
100%
Neural Network
100%
Neuroscience
Feedforward Neural Network
14%
Neural Network
100%
Medicine and Dentistry
Stone Formation
100%