In the recent years, a dramatic boost of the research is observed at the junction of photonics,
machine learning and artificial intelligence. A new methodology can be applied to the …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The tryptophan-kynurenine metabolic pathway plays the most essential role in tryptophan
metabolism, producing various endogenous bioactive molecules. The activation of the …

The integration of deep learning techniques and physics-driven designs is reforming the
way we address inverse problems, in which accurate physical properties are extracted from …

Machine learning models often fail to generalize well under distributional shifts.
Understanding and overcoming these failures have led to a research field of Out-of …

We review, for a general audience, a variety of recent experiments on extracting structure
from machine-learning mathematical data that have been compiled over the years. Focusing …

We introduce the use of reinforcement-learning (RL) techniques to the conformal-bootstrap
program. We demonstrate that suitable soft Actor-Critic RL algorithms can perform efficient …

There is great potential to apply machine learning in the area of numerical lattice quantum
field theory, but full exploitation of that potential will require new strategies. In this white …

Scope 1.1. Research questions to be answered from measurement data require algorithms.
A substantial part of physics research relies on algorithms that scientists develop as static …

Deep neural networks have been used to solve Ising models, including autoregressive
neural networks, convolutional neural networks, recurrent neural networks, and graph …