Related papers: Enhancing nuclear cross-section predictions with d…

This research explores the application of Deep Reinforcement Learning (DRL) to optimize the design of a nuclear fusion reactor. DRL can efficiently address the challenging issues attributed to multiple physics and engineering constraints…

The study of neutrino-nucleus interactions has recently received renewed attention due to their importance in interpreting the neutrino oscillation data. Over the past few years, there has been continuous disagreement between neutrino cross…

Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables…

DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned representations often enable state-of-the-art performance for downstream tasks, such as image…

Nuclei instance segmentation in histopathological images is of great importance for biological analysis and cancer diagnosis but remains challenging for two reasons. (1) Similar visual presentation of intranuclear and extranuclear regions…

For the operation of precision neutrino experiments, the understanding of neutrino interactions with matter is a preconditioned requirement for all detections and measurements of neutrinos. The largest uncertainties in estimating…

We present a distributed algorithm that enables a group of robots to collaboratively optimize the parameters of a deep neural network model while communicating over a mesh network. Each robot only has access to its own data and maintains…

Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often…

Diffusion models (DMs) have emerged as powerful foundation models for a variety of tasks, with a large focus in synthetic image generation. However, their requirement of large annotated datasets for training limits their applicability in…

A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of…

The Deep Operator Network (DeepONet) is a powerful neural operator architecture that uses two neural networks to map between infinite-dimensional function spaces. This architecture allows for the evaluation of the solution field at any…

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well…

This study presents a comprehensive model for neutrino deep inelastic scattering (DIS) cross sections spanning energies from 50 GeV to 5$\times10^{12}$ GeV with an emphasis on applications to neutrino telescopes. We provide calculations of…

Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources. Current methodologies primarily focus on compressing larger models yet at the expense of model…

With the development of digital twins and smart manufacturing systems, there is an urgent need for real-time distortion field prediction to control defects in metal Additive Manufacturing (AM). However, numerical simulation methods suffer…

The ultimate objectives of ongoing and upcoming neutrino experiments are the precise measurement of neutrino mixing parameters and the confirmation of mass hierarchy. The systematic inaccuracy in the cross-section models introduces…

Neutrino-nucleus cross-section measurements are critical for future neutrino oscillation analyses. However, our models to describe them require further refinement, and a deeper understanding of the underlying physics is essential for future…

Deep learning models (DLMs) frequently achieve accurate segmentation and classification of tumors from medical images. However, DLMs lacking feedback on their image segmentation mechanisms, such as Dice coefficients and confidence in their…

Critical transitions are the abrupt shifts between qualitatively different states of a system, and they are crucial to understanding tipping points in complex dynamical systems across ecology, climate science, and biology. Detecting these…

An updated set of (anti)neutrino-nucleon charged and neutral current cross sections at $3~{\rm GeV} \lesssim E_\nu \lesssim 100~{\rm GeV}$ is presented. These cross sections are of particular interest for the detector optimization and data…