Neural networks predict forces in jammed granular solids

Granular matter is all around us. Examples include sand, rice, nuts, coffee and even snow. These materials are made of solid particles that are large enough not to experience thermal fluctuations. Instead, their state is ...

Testing a machine learning approach to geophysical inversion

A common problem in the geosciences is the need to deduce unseen physical structure based on limited observations. For instance, a ground-penetrating radar observation attempts to infer underground structure without any in ...

Entanglement unlocks scaling for quantum machine learning

The field of machine learning on quantum computers got a boost from new research removing a potential roadblock to the practical implementation of quantum neural networks. While theorists had previously believed an exponentially ...

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