| description abstract | Machine intelligence integrates computation, data, models, and algorithms to solve problems that are too complex for humans. During the last three decades, machine intelligence has been a highly researched topic and widely used for solving complex real-world engineering problems. For instance, many engineering design problems can be formulated as optimization. Yet the curse of dimensionality with a large number of design variables makes the solution-searching process difficult. Similarly, to predict multiscale and multiphysics phenomena and control complex systems, models that are involved with many parameters will cause not only computational inefficiency but also inaccuracy due to the lack of data. Therefore, simplification and approximation strategies such as reduced-order modeling, surrogates, and linearization are commonly used. As a result, model-form and parameter uncertainties are inherent in the computational models for machine intelligence. Furthermore, the stochastic nature of complex systems, where random noise in the data is propagated to the models through parameter calibration and model identification, makes these analyses more challenging. The use of stochastic algorithms during the problem-solving process adds another layer of complexity for uncertainty quantification. | |
