Chen et al., 2024 - Google Patents
Enhanced Gaussian-mixture-model-based nonlinear probabilistic uncertainty propagation using Gaussian splitting approachChen et al., 2024
- Document ID
- 2228577533428092041
- Author
- Chen Q
- Zhang Z
- Fu C
- Hu D
- Jiang C
- Publication year
- Publication venue
- Structural and Multidisciplinary Optimization
External Links
Snippet
Practical engineering problems often involve stochastic uncertainty, which can cause substantial variations in the response of engineering products or even lead to failure. The coupling and propagation of uncertainty play a crucial role in this process. Hence, it is …
- 238000013459 approach 0 title description 14
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/206—Drawing of charts or graphs
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zaitseva et al. | Reliability analysis of multi-state system with application of multiple-valued logic | |
Cui et al. | Data‐driven model reduction for the Bayesian solution of inverse problems | |
Palar et al. | Global sensitivity analysis via multi-fidelity polynomial chaos expansion | |
Madankan et al. | Polynomial-chaos-based Bayesian approach for state and parameter estimations | |
Kat et al. | Validation metric based on relative error | |
Cao et al. | Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis | |
Hong et al. | Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model | |
Nannapaneni et al. | Uncertainty quantification in reliability estimation with limit state surrogates | |
Marzban | Variance-based sensitivity analysis: An illustration on the Lorenz'63 model | |
Wang et al. | Accelerated failure identification sampling for probability analysis of rare events | |
Wei et al. | Bounds optimization of model response moments: a twin-engine Bayesian active learning method | |
Alomair et al. | A new trigonometric modification of the Weibull distribution: Control chart and applications in quality control | |
Chowdhury et al. | High dimensional model representation for stochastic finite element analysis | |
Sandhu et al. | Nonlinear sparse Bayesian learning for physics-based models | |
Caiado et al. | Bayesian uncertainty analysis for complex physical systems modelled by computer simulators with applications to tipping points | |
Pandita et al. | Scalable fully Bayesian Gaussian process modeling and calibration with adaptive sequential Monte Carlo for industrial applications | |
Chen et al. | Enhanced Gaussian-mixture-model-based nonlinear probabilistic uncertainty propagation using Gaussian splitting approach | |
He et al. | An adaptive sparse polynomial dimensional decomposition based on Bayesian compressive sensing and cross-entropy | |
Baoyu et al. | Reliability analysis based on a novel density estimation method for structures with correlations | |
Manten et al. | Signature kernel conditional independence tests in causal discovery for stochastic processes | |
Putcha et al. | Reliability and risk analysis in engineering and medicine | |
Charisse Farr et al. | Combining opinions for use in Bayesian networks: A measurement error approach | |
Blomqvist et al. | Using Bayesian dynamical systems, model averaging and neural networks to determine interactions between socio-economic indicators | |
Yin et al. | An innovative DoE strategy of the kriging model for structural reliability analysis | |
Wang et al. | Extension of graph-accelerated non-intrusive polynomial chaos to high-dimensional uncertainty quantification through the active subspace method |