Skip to main navigation
Skip to search
Skip to main content
Universidad de los Andes Home
English
Español
Home
Profiles
Research units
Research output
Projects
Press/Media
Activities
Equipment
Student theses
Prizes
Courses
Impacts
Search by expertise, name or affiliation
Semi-autonomous neural networks differential equation solver
José Delpiano
, Pablo Zegers
Ingeniería Civil Eléctrica
Universidad de los Andes
Facultad de Ingeniería y Ciencias Aplicadas
Research output
:
Contribution to conference
›
Paper
2
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Semi-autonomous neural networks differential equation solver'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Complex Power Grid
50%
Complicated Geometries
50%
Criteria-based
50%
Differential Equation Solver
100%
Differential Equations
50%
Error Measures
50%
Finite Element Method
50%
First-order Equation
50%
Gridless Method
100%
Neural Network
100%
Number of Dimensions
50%
Partial Differential Equations
50%
Second-order Equation
50%
Semi-autonomous
100%
Simple Rules
50%
Solution of Equations
50%
Statistical Learning Theory
50%
Stopping Criterion
50%
Engineering
Boundary Condition
100%
Dimensional Problem
100%
Finite Element Analysis
100%
Gridless Method
100%
Partial Differential Equation
100%
Simple Rule
100%
Statistical Learning Theory
100%
Stopping Criterion
100%
Mathematics
Boundary Condition
50%
Differential Equation
100%
Dimensional Problem
50%
Finite Element Method
50%
Neural Network
100%
Partial Differential Equation
50%
Preceding Algorithm
50%
Second-Order Equation
50%