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MIT scientists have developed a deep learning technique that can determine the internal structure of materials from surface observations. This AI-based method provides a less expensive, noninvasive alternative for material inspection across various disciplines and is applicable even when materials are not fully understood. This approach could revolutionize everything from aircraft inspections to medical diagnostics. The team used a type of machine learning known as deep learning to compare a large set of simulated data about materials’ external force fields and the corresponding internal structure, and used that to generate a system that could make reliable predictions of the interior from the surface data.
