"Our guiding principle is this: the simplest machine learning model that explains the optimal design data is the best," says Ghattas. "Not just to make the technology more widely accessible but because in doing so we avoid overfitting the model to the data, which leads to the inability to predict unseen data."
"It's about making aerodynamic design optimization accessible to a much broader set of industrial users," he says. "We know these technologies can enable faster, cheaper and more energy efficient design and production methods. So they should also be universally available."
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