A new method for solving two-dimensional inverse scattering problems has been introduced by researchers Daoqi Liu, Tao Shan, Maokun Li, Fan Yang, and Shenheng Xu. The paper, titled "Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems," presents a deep learning-based imaging approach that combines electromagnetic

Recent research conducted by Zhipeng Wu, Mingzhi Fan, Yangjun Qin, Guangzu Zhang, and Nuo Yang has demonstrated a significant advancement in the thermal properties of plastic crystals. The paper titled "Achieving ultra-high anisotropy in thermal conductivity of plastic crystal through megapascal pressure of hot pressing" details how applying

A new model for generating axisymmetric concentrated vortices has been introduced by researchers O.G. Onishchenko, S.N. Artekha, F.Z. Feygin, and N.M. Astafieva in their paper titled "Generation Model of a Spatially Limited Vortex in a Stratified Unstable Atmosphere". This model addresses the dynamics of

Recent research by Yuanqing Wang and colleagues, titled "On the design space between molecular mechanics and machine learning force fields," explores the intersection of molecular mechanics (MM) and machine learning force fields (MLFFs). The study highlights the ongoing challenge of creating a force field that combines the accuracy

Recent research by Mohamad Ali-Dib and Kristen Menou, titled "Physics simulation capabilities of LLMs," evaluates the performance of state-of-the-art Large Language Models (LLMs) in solving advanced physics problems. The study, submitted to arXiv on December 4, 2023, investigates how LLMs can tackle PhD-level computational physics challenges using established

A recent paper titled "A generalized adaptive central-upwind scheme for compressible flow simulations and preventing spurious vortices" by Amareshwara Sainadh Chamarthi introduces a new adaptive central-upwind scheme aimed at enhancing the simulation of compressible flows, particularly in scenarios with discontinuities. This novel approach reportedly improves computational efficiency compared

A new framework called ( \mu \text{GUIDE} ) has been introduced for quantitative imaging, particularly in the context of diffusion-weighted MRI. This framework utilizes a Bayesian approach to estimate posterior distributions of tissue microstructure parameters from various biophysical models or MRI signal representations. The authors, Maëliss Jallais and Marco Palombo, highlight

Recent research conducted by Tomek Jaroslawski, Divya Jaganathan, Rama Govindarajan, and Beverley McKeon presents significant findings regarding particle settling dynamics in fluid environments. The paper, titled "Basset-Boussinesq history force and inertia are relevant for unsteady particle settling dynamics," was submitted to arXiv on August 22, 2024, and revised

A recent paper titled "Towards Quantum Simulation of Non-Markovian Open Quantum Dynamics: A Universal and Compact Theory" has been published by authors Xiang Li, Su-Xiang Lyu, Yao Wang, Rui-Xue Xu, Xiao Zheng, and YiJing Yan. This research addresses the complexities associated with non-Markovianity in open quantum systems, which

Recent advancements in optical communication have been highlighted in a new paper titled "Demonstration of Beyond Terabit/s/lambda Nonlinearity-free Transmission over the Hollow-core Fibre" by Yang Hong and colleagues. The study presents a method for transmitting signals at speeds exceeding one terabit per second per wavelength through

Recent research by Gennaro Ciampa, Giulio G. Giusteri, and Alessio G. Soggiu introduces new models for viscoelastic materials, which include both solids and fluids, based on logarithmic stresses. This approach aims to better capture the elastic contributions to material responses. The study highlights the use of the matrix logarithm to

Recent advancements in lensless microscopy have been made with the introduction of a novel method called sparsity-regularized coded ptychography. This technique aims to enhance imaging efficiency while maintaining high-quality results. The research, conducted by Ninghe Liu, Qianhao Zhao, and Guoan Zheng, presents a new approach termed the ptychographic proximal total-variation