How Deep Learning Is Transforming Engineering Problem-Solving and Predictive Modeling

The traditional way to solve engineering problems is based on experimental testing and engineering experience, from which analytical equations are derived and ultimately evolve into numerical simulations. Although these research methods are reliable, they are expensive. The equipment and samples for physical experiments, time-consuming simulations, the high cost of computation, and the difficulty in dealing with extremely complex computer systems are all great challenges to completing the research. Instead, deep learning is a method that can automatically drive data, identify engineering patterns, and accelerate predictive decision-making in response to increasingly complex engineering problems.

Deep learning is an extension of the standard physical methods used in engineering projects or research. The neural networks are trained on experimental or simulated data in order to recognize and analyze patterns. Thus, prediction of engineering results is made possible. This means that “deep learning” adds an analysis of pooling to the experiments or simulations. Even so, deep learning cannot solve physical equations; it learns the relationships between different parameters from the existing data.

Deep learning can be used to predict the different mechanical behaviors of materials or structures, the effects of different material combinations, and the mechanical properties of materials based on their composition. It can also anticipate possible failures, thus improving the safety of materials and structures. Also, it is able to detect patterns in designs and create engineering designs. Nevertheless, deep learning is a data-driven method, which relies on a large amount of data. Deep learning cannot fully substitute physics without the high-quality experimental/simulation data because it does not possess physical interpretability by itself. Even so, engineers still find autonomous engineering designs indispensable in solving engineering problems, efficiently evaluating engineering projects, and offering solutions.

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