More information Zitong Ye et al, Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network, Intelligent Computing ...

Physics-Informed Neural Networks (PINNs) augment traditional neural architectures by embedding the governing equations of physical systems directly into the loss function. Instead of solely minimising ...

Machine learning is rapidly reshaping how we model molecules, and a growing body of work suggests that neural networks are not merely statistical ...

Short video shows the neural network training results and reproduction of flocking from real-world data. Credit: Cell Reports Physical Science Learning local rules with physics-informed AI To address ...

(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 ...