High-Order Modulation Large MIMO Detector Based on Physics-Inspired MethodsApplying quantum annealing or current quantum-/physics-inspired algorithms for MIMO detection always abandon the direct gray-coded bit-to-symbol mapping in order to obtain Ising form, leading to…Qing-Guo Zeng, Xiao-Peng Cui, Xian-Zhe Tao et al.·Feb 24, 2025SaveLearn
Application of the Pathline Method to the Aircraft Reactor ExperimentIn this work, a new numerical method for the transport of Delayed Neutron Precursors (DNPs) is applied to the Aircraft Reactor Experiment (ARE). The pathline method is based on the Method of…Mathis Caprais, Nathan Greiner, André Bergeron·Feb 24, 2025SaveLearn
CardSharp: A python library for generating MCNP6 input decksA python library for the creation of MCNP6 input decks is described. The library supports geometry generation with automatic assignment of surface/facet numbers, cell numbers, transform numbers and…Nikhil Deshmukh, Mital Zalavadia·Feb 23, 2025SaveLearn
A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAMLX-ray absorption spectroscopy (XAS) is a powerful technique to probe the electronic and structural properties of materials. With the rapid growth in both the volume and complexity of XAS datasets…Xue Han, Haodong Yao, Fei Zhan et al.·Feb 23, 2025SaveLearn
DiffChip: Thermally Aware Chip Placement with Automatic DifferentiationChiplets are modular integrated circuits that can be combined to form a larger system, offering flexibility and performance enhancements. However, their dense packing often leads to significant…Giuseppe Romano, Aakrati Jain, Nima Dehmamy et al.·Feb 23, 2025SaveLearn
Convergence of Body-Orders in Linear Atomic Cluster ExpansionsWe study the convergence of a linear atomic cluster expansion (ACE) potential with respect to its basis functions, in terms of the effective two-body interactions of elemental Carbon and Silicon…Apolinario Miguel Tan, Franco Pellegrini, Stefano de Gironcoli·Feb 22, 2025SaveLearn
Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystalsAs with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for the accurate and…Flaviano Della Pia, Benjamin X. Shi, Venkat Kapil et al.·Feb 21, 2025SaveLearn
PhyLiNO: A Forward-Folding Likelihood-Fit Framework for Neutrino Oscillation PhysicsWe present a framework for the analysis of data from neutrino oscillation experiments. The framework performs a profile likelihood fit and employs a forward-folding technique to optimize its model…Denise Hellwig, Stefan Schoppmann, Philipp Soldin et al.·Feb 21, 2025SaveLearn
Enhancing nuclear cross-section predictions with deep learning: the DINo algorithmAccurate modeling of nuclear reaction cross-sections is crucial for applications such as hadron therapy, radiation protection, and nuclear reactor design. Despite continuous advancements in nuclear…Levana Gesson, Greg Henning, Jonathan Collin et al.·Feb 20, 2025SaveLearn
A note on the lattice momentum balance in the lattice Boltzmann interaction-frameworkIn this note, we show how the exploitation of the lattice momentum balance condition allows to envisage an analytical procedure to define the lattice pressure tensor (LPT) for the multi-phase…Francesca Pelusi, Matteo Lulli, Christophe Coreixas et al.·Feb 20, 2025SaveLearn
Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global EncodingGraph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inherently local and can…Alessandro Caruso, Jacopo Venturin, Lorenzo Giambagli et al.·Feb 19, 2025SaveLearn
Optimization of the Woodcock Particle Tracking Method Using Neural NetworkThe acceptance rate in Woodcock tracking algorithm is generalized to an arbitrary position-dependent variable q(x). A neural network is used to optimize q(x), and the FOM value is used as the…Bingnan Zhang·Feb 19, 2025SaveLearn
Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructuresProbing the ideal limit of interfacial thermal conductance (ITC) in two-dimensional (2D) heterointerfaces is of paramount importance for assessing heat dissipation in 2D-based nanoelectronics. Using…Ting Liang, Ke Xu, Penghua Ying et al.·Feb 19, 2025SaveLearn
CooLBM: A Collaborative Open-Source Reactive Multi-Phase/Component Simulation Code via Lattice Boltzmann MethodThe current work presents a novel COllaborative Open-source Lattice Boltzmann Method framework, so-called CooLBM. The computational framework is developed for the simulation of single and…R. Alamian, A. K. Nayak, M. S. Shadloo·Feb 18, 2025SaveLearn
An Adaptive Model Order Reduction Approach for the Finite Element Method in Time Domain in ElectromagneticsTime domain simulations are crucial for analyzing transient behavior and broadband responses in electromagnetic problems. However, conventional numerical methods such as finite element method in time…Ruth Medeiros, Valentín de la Rubia·Feb 18, 2025SaveLearn
Monotone conservative strategies in data assimilationThis paper studies whether numerically preserving monotonic properties can offer modelling advantages in data assimilation, particularly when the signal or data is a realization of a stochastic…James Woodfield·Feb 18, 2025SaveLearn
Learning Smooth and Expressive Interatomic Potentials for Physical Property PredictionMachine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on…Xiang Fu, Brandon M. Wood, Luis Barroso-Luque et al.·Feb 17, 2025SaveLearn
Analysis of the autocorrelation function for time series with higher-order temporal correlations: An exponential caseTemporal correlations in the time series observed in various systems have been characterized by the autocorrelation function. Such correlations can be explained by heavy-tailed interevent time…Min-ho Yu, Hang-Hyun Jo·Feb 16, 2025SaveLearn
EVODMs: variational learning of PDEs for stochastic systems via diffusion models with quantified epistemic uncertaintyWe present Epistemic Variational Onsager Diffusion Models (EVODMs), a machine learning framework that integrates Onsager's variational principle with diffusion models to enable thermodynamically…Zequn He, Celia Reina·Feb 14, 2025SaveLearn
Collision-Based Hybrid Method for Two-Dimensional Neutron Transport ProblemsA collision-based hybrid method for the discrete ordinates approximation of the multigroup neutron transport equation is developed for two-dimensional time-dependent problems. At each time step, this…Ben Whewell, Ryan G. McClarren·Feb 14, 2025SaveLearn
NextGenPB: an analytically-enabled super resolution and local (de)refinement Poisson-Boltzmann Equation solverThe Poisson-Boltzmann equation (PBE) is a relevant partial differential equation commonly used in biophysical applications to estimate the electrostatic energy of biomolecular systems immersed in…Vincenzo Di Florio, Patrizio Ansalone, Sergii V. Siryk et al.·Feb 13, 2025SaveLearn
Magnetic mesh generation and field line reconstruction for scrape-off layer and divertor modeling in stellaratorsThe design of divertor targets and baffles for optimal heat and particle exhaust from magnetically confined fusion plasmas requires a combination of fast, low-fidelity models (such as EMC3-lite [1])…H. Frerichs, D. Boeyaert, Y. Feng et al.·Feb 12, 2025SaveLearn
Consistent Solutions of the Radiation Diffusion Equation in Spherical and Cylindrical GeometriesWe have extended the radiation diffusion model of Hammer and Rosen to diverging spherical and cylindrical geometries. The effect of curvilinear geometry on the supersonic, expanding wavefront…Ethan Smith, Evan Bursch, Ryan G. McClarren·Feb 11, 2025SaveLearn
Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics ModelingTo meet climate targets, the IPCC underscores the necessity of technologies capable of removing gigatonnes of CO2 annually, with Geological Carbon Storage (GCS) playing a central role. GCS involves…Abhinav Prakash Gahlot, Felix J. Herrmann·Feb 11, 2025SaveLearn
Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow TechnologyGeological Carbon Storage (GCS) is a key technology for achieving global climate goals by capturing and storing CO2 in deep geological formations. Its effectiveness and safety rely on accurate…Abhinav Prakash Gahlot, Rafael Orozco, Felix J. Herrmann·Feb 11, 2025SaveLearn