This article introduces a special issue on the interaction between the rapidly expanding field
of machine learning and ongoing research in physics. The first half of the papers in this …

Particle-based deep generative models, such as gradient flows and score-based diffusion
models, have recently gained traction thanks to their striking performance. Their principle of …

Complex networks pervade various real-world systems, from the natural environment to
human societies. The essence of these networks is in their ability to transition and evolve …

Abstract Formulation development is a critical step in the development of medicines. The
process requires human creativity, ingenuity and in-depth knowledge of formulation …

Electroencephalography (EEG) is an invaluable tool in neuroscience, offering insights into
brain activity with high temporal resolution. Recent advancements in machine learning and …

Physical adversarial examples (PAEs) are regarded as" whistle-blowers" of real-world risks
in deep-learning applications. However, current PAE generation studies show limited …

We aimed to build an MR-CT interconversion model based on structure-constraints Cycle-
constraints Generative Adversarial Neural Networks (scCycleGANs). We used MDCT …

Abstract Generative Adversarial Networks (GANs) have emerged as a powerful tool for 3D
voxel image synthesis, particularly through conditional GANs (cGANs). This paper presents …