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February 2023
ISSN 1530-9827
EISSN 1944-7078
In this Issue
Special Issue: Machine Intelligence for Engineering Under Uncertainties
Editorial
Special Issue: Machine Intelligence for Engineering Under Uncertainties
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 010201.
doi: https://doi.org/10.1115/1.4056396
Topics:
Artificial intelligence
,
Uncertainty
Research Papers
Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011001.
doi: https://doi.org/10.1115/1.4054039
A Framework for Inverse Prediction Using Functional Response Data
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011002.
doi: https://doi.org/10.1115/1.4053752
Topics:
Errors
,
Nuclear forensics
,
Particulate matter
,
Scalars
,
Shapes
,
Simulation
,
Uncertainty
,
Texture (Materials)
,
Modeling
,
Uncertainty quantification
A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011003.
doi: https://doi.org/10.1115/1.4054501
Topics:
Optimization
,
Wakes
,
Wind farms
,
Uncertainty
,
Turbines
,
Wind
,
Wind turbines
A Priori Denoising Strategies for Sparse Identification of Nonlinear Dynamical Systems: A Comparative Study
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011004.
doi: https://doi.org/10.1115/1.4054573
Topics:
Errors
,
Filters
,
Filtration
,
Noise (Sound)
,
Splines
,
Algorithms
,
Smoothing methods
,
Nonlinear dynamical systems
,
Polynomials
Uncertainty Quantification and Optimal Robust Design for Machining Operations
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011005.
doi: https://doi.org/10.1115/1.4055039
Topics:
Design
,
Machining
,
Stability
,
Uncertainty
,
Risk
Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011006.
doi: https://doi.org/10.1115/1.4055315
Topics:
Calibration
,
Blades
Characterizations and Optimization for Resilient Manufacturing Systems With Considerations of Process Uncertainties
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011007.
doi: https://doi.org/10.1115/1.4055425
Topics:
Downtime
,
Manufacturing systems
,
Optimization
,
Production planning
,
Uncertainty
,
Simulation
,
Manufacturing
,
Machinery
,
Polynomials
,
Chaos
A Multi-Fidelity Approach for Reliability Assessment Based on the Probability of Classification Inconsistency
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011008.
doi: https://doi.org/10.1115/1.4055508
Topics:
Failure
,
Heat exchangers
,
Probability
,
Reliability
,
Support vector machines
,
Shells
,
Algorithms
Acceleration of a Physics-Based Machine Learning Approach for Modeling and Quantifying Model-Form Uncertainties and Performing Model Updating
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011009.
doi: https://doi.org/10.1115/1.4055546
Topics:
Approximation
,
Computer simulation
,
Construction
,
Dimensions
,
Machine learning
,
Modeling
,
Nozzles
,
Physics
,
Robustness
,
Simulation
Spatial Transform Depthwise Over-Parameterized Convolution Recurrent Neural Network for License Plate Recognition in Complex Environment
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011010.
doi: https://doi.org/10.1115/1.4055507
Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011011.
doi: https://doi.org/10.1115/1.4055852
Topics:
Machine learning
,
Materials science
,
Physics
,
Stress
,
Testing
,
Crystals
,
Plasticity
,
Simulation
,
Noise (Sound)
,
Finite element analysis
Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011012.
doi: https://doi.org/10.1115/1.4055924
Technical Briefs
A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-Box Design Problems
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 014501.
doi: https://doi.org/10.1115/1.4054480
Topics:
Design
,
Optimization
,
Regression models
,
Errors
A Quantitative Insight Into the Role of Skip Connections in Deep Neural Networks of Low Complexity: A Case Study Directed at Fluid Flow Modeling
J. Comput. Inf. Sci. Eng. February 2023, 23(1): 014502.
doi: https://doi.org/10.1115/1.4054868
Topics:
Artificial neural networks
,
Modeling
,
Fluid dynamics
,
Errors
,
Testing
,
Permeability
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