This review focuses on recent advancements in data-driven methods for analyzing flow and
transport in porous media, which are showing promising potential for applications in energy …

We present a novel approach for the integration of scattering cross sections and the
generation of partonic event samples in high-energy physics. We propose an importance …

Non-Markovian noise presents a particularly relevant challenge in understanding and
combating decoherence in quantum computers, yet is challenging to capture in terms of …

Quantum control, which refers to the active manipulation of physical systems described by
the laws of quantum mechanics, constitutes an essential ingredient for the development of …

Energy conservation is a basic physics principle, the breakdown of which often implies new
physics. This paper presents a method for data-driven “new physics” discovery. Specifically …

Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in
fundamental and applied science. However, training and building reliable ANN models …

Traveling waves have been measured at a diversity of regions and scales in the brain,
however a consensus as to their computational purpose has yet to be reached. An intriguing …

Neural networks have recently gained attention in the context of solving inverse problems.
Physics-Informed Neural Networks (PINNs) are a prominent methodology for the task of …

Quantum computer provides new opportunities for quantum chemistry. In this article, we
present a versatile, extensible, and efficient software package, named Q $^ 2$ Chemistry, for …

We present a deep generative model, named Monge-Amp\ere flow, which builds on
continuous-time gradient flow arising from the Monge-Amp\ere equation in optimal transport …