Machine intelligence (MI) integrates computation, data, models, and algorithms to solve problems that are too complex for humans. During the last three decades, MI has been a highly researched topic and widely used for solving complex real-world engineering problems. The main theme of this Special Issue is dedicated to the development of MI methods that sheds a new light for solving problems deemed difficult in engineering sciences under uncertainties.
Many real-world engineering design problems can be formulated as optimization. Yet the curse of dimensionality with a large number of design variables, both discrete and continuous, makes the solution searching process difficult. Furthermore, interpretation of the large amount of simulation and experimental data needs advanced computation, data mining, Big Data analytics, and deep learning methodologies. The stochastic nature of real-world engineering systems makes these analyses even more challenging. Due to their complexity, real-world problems are difficult to solve using derivative-based local optimization algorithms. In the recent past, MI and its branches have been used to solve complex real-world engineering problems that cannot be solved using conventional methods.
This Special Issue strives to gather the latest developments of MI applications in real-world engineering systems, particularly the ones under uncertainty. On this basis, this Special Issue includes key applications of MI on different engineering disciplines such as engineering design, monitoring and maintenance, structural systems, applied
mechanics, etc. Theories, methodologies, tools, computational aspects for MI topics include (but are not limited to):
Topic Areas
• Mathematical foundation of machine learning under uncertainties
• Probabilistic methods and statistical tools for scientific machine learning
• Neural networks and deep learning with probabilistic reasoning
• Genetic programming and evolved systems with uncertainties
• Evolutionary and Swarm Intelligence with uncertainties for multi-objective problems
• Stochastic and robust optimization using intelligent search methods
• Randomized algorithms (stochastic gradient, compressed sensing, etc.)
• Stochastic surrogate/metamodels with model-form and parameter uncertainties
• Machine learning based on emerging computing hardware
• Data-driven statistical inverse problems
• Data mining, pattern recognition, and data clustering
• Fuzzy control, optimization, and decision making under uncertainties
• Applications of MI in product engineering such as engineering mechanics, system dynamics, reliability
• Applications of MI in process engineering such as scheduling, system monitoring, maintenance, optimal control
TABLE OF CONTENTS
EDITORIAL
Special Issue: Machine Intelligence for Engineering Under Uncertainties
Amir H. Gandomi, Marc Mignolet, Christian Soize, Yan Wang, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 010201. doi: https://doi.org/10.1115/1.
RESEARCH PAPERS
Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study
Ying Zhao, Chen Jiang, Manuel A. Vega, Michael D. Todd, Zhen Hu, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011001. doi: https://doi.org/10.1115/1.
A Framework for Inverse Prediction Using Functional Response Data
Daniel Ries, Adah Zhang, J. Derek Tucker, Kurtis Shuler, Madeline Ausdemore, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011002. doi: https://doi.org/10.1115/1.
A Probabilistic Learning Approach Applied to the Optimization of Wake Steering in Wind Farms
Jeferson O. Almeida, Fernando A. Rochinha, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011003. doi: https://doi.org/10.1115/1.
Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011004. doi: https://doi.org/10.1115/1.
Uncertainty Quantification and Optimal Robust Design for Machining Operations
Jinming Wan, Yiming Che, Zimo Wang, Changqing Cheng, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011005. doi: https://doi.org/10.1115/1.
Multi-Level Bayesian Calibration of a Multi-Component Dynamic System Model
Berkcan Kapusuzoglu, Sankaran Mahadevan, Shunsaku Matsumoto, Yoshitomo Miyagi, Daigo Watanabe, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011006. doi: https://doi.org/10.1115/1.
Characterizations and Optimization for Resilient Manufacturing Systems With Considerations of Process Uncertainties
Qiyang Ma, Yiming Che, Changqing Cheng, Zimo Wang, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011007. doi: https://doi.org/10.1115/1.
Bharath Pidaparthi, Samy Missoum, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011008. doi: https://doi.org/10.1115/1.
Marie-Jo Azzi, Chady Ghnatios, Philip Avery, Charbel Farhat, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011009. doi: https://doi.org/10.1115/1.
Jiehang Deng, Haomin Wei, Zhenxiang Lai, Guosheng Gu, Zhiqiang Chen, Leo Chen, Lei Ding, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011010. doi: https://doi.org/10.1115/1.
Anh Tran, Kathryn Maupin, Theron Rodgers, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011011. doi: https://doi.org/10.1115/1.
Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction
Luka Malashkhia, Dehao Liu, Yanglong Lu, Yan Wang, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 011012. doi: https://doi.org/10.1115/1.
TECHNICAL BRIEFS
Arpan Biswas, Claudio Fuentes, Christopher Hoyle, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 014501. doi: https://doi.org/10.1115/1.
Abouzar Choubineh, Jie Chen, Frans Coenen, Fei Ma, J. Comput. Inf. Sci. Eng. February 2023, 23(1): 014502. doi: https://doi.org/10.1115/1.
