Hence, researchers often simulate the brain as a network of coupled neural masses, each described by a mean-field model. These models capture the essential features of neuronal populations while ...

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...

Stochastic-Numerical-methods-for-the-Schlogl-model Stochastic-Numerical-methods-for-the-Schlogl-model Public Forked from karenml/Stochastic-Numerical-methods-for-the-Schlogl-model Project undertaken ...

In a mesmerising blend of art and mathematics, a research student from University College London (UCL) has recently gained widespread attention for creating intricate images solely through ...

Abstract: This work develops and analyzes a class of adaptive biased stochastic optimization (ABSO) algorithms from the perspective of the GEneralized Adaptive gRadient (GEAR) method that contains ...

GANs are often criticized for being difficult to train, with their architectures relying heavily on empirical tricks. Despite their ability to generate high-quality images in a single forward pass, ...