261 papers
This section explores the intersection where physics meets data analysis, a rapidly evolving frontier where complex datasets reveal hidden patterns in the universe. From tracking particle collisions to modeling cosmic structures, these studies rely on advanced statistical methods to turn raw numbers into fundamental insights about how reality works.
Gist.Science monitors every new preprint in this category as it appears on arXiv, ensuring you never miss a breakthrough. We process each entry to provide both plain-language overviews for general understanding and detailed technical summaries for experts, bridging the gap between dense research and clear comprehension.
Below are the latest papers in physics and data analysis, organized for easy reading and discovery.
This paper presents a novel, computationally efficient nonlinear unsteady aerodynamic reduced-order model that integrates oscillator dynamics with Volterra theory to accurately reproduce complex aeroelastic shock buffet phenomena, such as lock-in, in three-dimensional transonic flows.
This paper demonstrates that high-accuracy, reference-free material classification using sparse-frequency terahertz spectroscopy can be achieved by applying data-driven feature selection algorithms to identify discriminative absorption bands, thereby eliminating the need for broadband sources and reference measurements for compact sensor applications.
This paper presents a novel algorithm that determines the directionality of 2D discrete data distributions by rotating a reference matrix to minimize the Frobenius norm of the difference, a process generalized via a derived continuous analog (CFND) that approximates the relationship as an absolute sine function for applications in fields like neutrino detection and astronomy.
This paper introduces and experimentally validates "Dichography," a method that algorithmically separates overlapping diffraction signals from two time-delayed, multi-color X-ray pulses to reconstruct two distinct ultrafast images of nanoscale samples from a single detector pattern, thereby enabling the capture of structural dynamics before significant radiation damage occurs.
This paper introduces Geometric Autoencoders for Bayesian Inversion (GABI), a framework that learns geometry-aware generative models from large datasets of varying physical systems to serve as informative priors for robust, well-calibrated uncertainty quantification in ill-posed inverse problems without requiring knowledge of governing equations.
This paper introduces NuBench, an open benchmark comprising seven large-scale simulated datasets across six detector geometries, designed to facilitate the development and comparative evaluation of deep learning-based event reconstruction methods for neutrino telescopes.
This paper proposes a novel Multi-Input Multi-Output (MIMO) Extreme Learning Machine (ELM) approach for short-term energy forecasting in Corsica that leverages sliding windows and cyclic time encoding to achieve high accuracy and computational efficiency for real-time applications, significantly outperforming persistence models while offering a closed-form solution superior to deep learning methods like LSTM.
This review presents the first unified conceptual framework for scientific machine learning in hydrology, organizing fragmented methodological families into a coherent structure to clarify novelty, foster cumulative progress, and guide future research directions.
This paper presents a systematic Monte Carlo-based uncertainty propagation analysis for a spherical magnetic field camera, quantifying how sensor calibration and positioning errors impact the spatial distribution of field estimation uncertainty to identify dominant error sources and assess the robustness of spherical harmonic methods.
This paper presents a hybrid framework that combines a physics-guided First-Order Shear Deformation Theory model with machine learning and uncertainty quantification to achieve accurate, robust, and data-efficient impact localisation and force estimation in composite plates.