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Two new approaches allow deep neural networks to solve entire families of partial differential equations, making it easier to model complicated systems and to do so orders of magnitude faster.

To the surprise of experts in the field, a postdoctoral statistician has solved one of the most important problems in high-dimensional convex geometry.

Even as mathematicians and computer scientists proved big results in computational complexity, number theory and geometry, computers proved themselves increasingly indispensable in mathematics.

A small community of mathematicians is using a software program called Lean to build a new digital repository. They hope it represents the future of their field.

Computer scientists are trying to build an AI system that can win a gold medal at the world’s premier math competition.

AI tools are shaping next-generation theorem provers, and with them the relationship between math and machine.

At the molecular level, glass looks like a liquid. But an artificial neural network has picked up on hidden structure in its molecules that may explain why glass is rigid like a solid.

Neural networks have been taught to quickly read the surfaces of proteins — molecules critical to many biological processes.

After translating some of math’s complicated equations, researchers have created an AI system that they hope will answer even bigger questions.

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