The Navier–Stokes partial differential equation was developed in the early 19th century by Claude-Louis Navier and George ...

In our increasingly electrified world, supercapacitors have emerged as critical components in transportation and renewable ...

Physics-informed neural networks (PINNs) represent a burgeoning paradigm in computational science, whereby deep learning frameworks are augmented with explicit physical laws to solve both forward and ...

Resilient energy systems depend on reliable batteries. The lithium-ion (Li-ion) batteries powering our world must endure the steady strain of time, charge cycles, and environmental conditions that ...

(A–C) Representative images reconstructed by conventional method (left) and new method (right) of microtubules, nuclear pore complexes and F-actin samples. The regions enclosed by the white boxes are ...

Metal additive manufacturing (AM) experiments are slow and expensive. Engineers are using physics-informed neural networks to predict the outcomes of complex processes involved in AM. The team trained ...

The use of artificial intelligence is already becoming commonplace in physics, but could physics also help AI? Tara Shears examines the relationship between the two fields following a survey and ...