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Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high…
Climate models play a critical role in understanding and projecting climate change. Due to their complexity, their horizontal resolution of about 40-100 km remains too coarse to resolve processes such as clouds and convection, which need to…
A direct and local deep learning (DL) model for atomic forces is presented. We demonstrate the model performance in bulk aluminum, sodium, and silicon; and show that its errors are comparable to those found in state-of-the-art machine…
The differential cross section for the $^{1}$H$(d,pp)n$ breakup reaction at deuteron beam energy of 380 MeV has been determined with high precision for 189 angular configurations of outgoing protons in the region of forward laboratory…
We perform a comprehensive analysis of complete fusion cross section data with the aim to derive, in a completely data-driven way, a model suitable to predict the integrated cross section of the fusion between light to medium mass nuclei at…
Nuclear cross sections are basic inputs to any nuclear computation. Campaigns of experiments are fitted with the parametric R-matrix model of quantum nuclear interactions, and the resulting cross sections are documented - both point-wise…
Separating and labeling each instance of a nucleus (instance-aware segmentation) is the key challenge in segmenting single cell nuclei on fluorescence microscopy images. Deep Neural Networks can learn the implicit transformation of a…
High energy photoproduction off nuclear targets is studied within the Glauber-Gribov approximation. The photon is assumed to interact as a $q\bar{q}$-system according to the Generalized Vector Dominance Model and as a ``bare photon'' in…
This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy…
Interactions of protons with nuclei are modeled in a form that is suitable for Monte Carlo simulation of proton transport. The differential cross section (DCS) for elastic collisions of protons with neutral atoms is expressed as the product…
The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector…
Feature engineering has been the key to the success of many prediction models. However, the process is non-trivial and often requires manual feature engineering or exhaustive searching. DNNs are able to automatically learn feature…
In the study of nuclear cross sections, the computational demands of data assimilation methods can become prohibitive when dealing with large data sets. We have developed a novel variant of the data thinning algorithm, inspired by the…
With the aim at quantitatively investigating the longstanding problem concerning the effect of short range nucleon-nucleon correlations on scattering processes at high energies, the total neutron-nucleus cross section is calculated within a…
An interesting physical process has been unveiled: dynamical core excitation during a breakup reaction of loosely bound $core+N$ systems. These reactions are typically used to extract spectroscopic information and/or astrophysical…
We present our deep learning framework to solve and accelerate the Time-Dependent partial differential equation's solution of one and two spatial dimensions. We demonstrate DiffusionNet solver by solving the 2D transient heat conduction…
A few analytic exponential models of elastic differential cross section, constructed as purely phenomenological models, are proposed and tested. The models incorporate energy-dependent exponential slopes, power-law prefactors, and localized…
Protein contacts provide key information for the understanding of protein structure and function, and therefore contact prediction from sequences is an important problem. Recent research shows that some correctly predicted long-range…
Thermal effects for inelastic neutrino-nucleus scattering off even-even nuclei in the iron region are studied. Allowed and first-forbidden contributions to the cross sections are calculated within the quasiparticle random phase…
Data scarcity hinders deep learning for medical imaging. We propose a framework for breast cancer classification in thermograms that addresses this using a Diffusion Probabilistic Model (DPM) for data augmentation. Our DPM-based…