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Engineering analysis is used to simulate and predict the performance of a design with confidence, explore design modifications, and inform stakeholders. To do so, models of the real world are needed. There are two camps for achieving high-fidelity simulations: the physics-based crowd and the data-driven machine learning crowd. The former is more traditional and involves solving governing equations based on physical principles, while the latter is more experimental and involves utilizing machine learning algorithms to build models based on observed or simulated data. Both camps have their own biases, but a combination of the two can be used to achieve the best results.
