MIT researchers have developed a machine-learning technique that uses an image of the material's internal structure to estimate the stresses and strains acting on the material. The advance could accelerate engineers' design process by eliminating the need to solve complex equations. Isaac Newton may have met his match. For centuries, engineers have relied on physical laws -- developed by Newton and others -- to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials.

Isaac Newton may have met his match. For centuries, engineers have relied on physical laws -- developed by Newton and others -- to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials. MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.

Isaac Newton may have met his match. For centuries, engineers have relied on physical laws--developed by Newton and others--to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials. MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.