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 ...

Artificial intelligence (AI) systems, particularly artificial neural networks, have proved to be highly promising tools for ...

(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 ...

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 ...

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 ...

Optical coherence tomography (OCT) is an imaging technology that can non-invasively generate cross-sectional images of tissue. OCT is widely used in eye clinics to diagnose and manage retinal diseases ...