Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Search content at Experts@Minnesota
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Datasets
Press/Media
Activities
Fellowships, Honors, and Prizes
Impacts
Probability-based design optimization of dynamic systems
Turuna S. Seecharan
, Gordon J. Savage
Mechanical & Industrial Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
1
Link opens in a new tab
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Probability-based design optimization of dynamic systems'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
System Dynamics
100%
Mechanistic Model
100%
Design Optimization
100%
Probability-based Design
100%
Performance Measures
66%
Metamodel
66%
Time-invariant
66%
Specific Information
66%
Probability Distribution
33%
Monte Carlo Method
33%
System Reliability
33%
Discrete-time
33%
Control Mechanism
33%
Training Set
33%
Random Variables
33%
Time Reduction
33%
Error Analysis
33%
Distribution Parameter
33%
Probability Space
33%
Small Matrices
33%
Response Matrix
33%
Singular Value Decomposition
33%
Standard Normal
33%
Monte Carlo Sampling
33%
Explicit Model
33%
Servo Control
33%
Function Approximation
33%
Computer Experiments
33%
Variable States
33%
Design of Computer Experiments
33%
Probabilistic Problems
33%
First-order Reliability Method
33%
Lifetime Simulation
33%
Invariant Probabilities
33%
Normal Probability
33%
Limit State Function
33%
Reliability Method
33%
Probability Problems
33%
Mathematics
Probability
100%
Matrix
66%
Performance Measure
33%
Probability Distribution
16%
Singular Value Decomposition
16%
Error Analysis
16%
Probability Space
16%
Distribution Parameter
16%
Normal Probability
16%
Discrete Time
16%
Control System
16%
Monte Carlo
16%
State Function
16%
Training Set
16%
Random Variable
16%
Monte Carlo Method
16%