physics.data-an papers | Gist.Science

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.

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact

This paper presents an efficient ensemble-based data assimilation framework that combines Smoothed Particle Hydrodynamics and the ensemble Kalman filter to automatically calibrate critical material model parameters for high-velocity impact simulations using data from a single test, demonstrating superior computational efficiency over traditional methods while providing diagnostic insights into parameter sensitivity and identifiability.

Rong Jin, Guangyao Wang, Xingsheng Sun2026-04-01🔬 cond-mat.mtrl-sci

MCbiF: Measuring Topological Autocorrelation in Multiscale Clusterings via 2-Parameter Persistent Homology

This paper introduces the Multiscale Clustering Bifiltration (MCbiF), a 2-parameter topological framework that encodes non-hierarchical multiscale clusterings to extract stable, interpretable features via multiparameter persistent homology, demonstrating superior performance in machine learning tasks and real-world applications compared to existing methods.

Juni Schindler, Mauricio Barahona2026-04-01🔬 physics

Declarative bespoke modelling: A new approach

The paper proposes "Declarative Bespoke Modelling," a new paradigm where modellers explicitly define input-output relationships to achieve perfect predictive accuracy, unconditional stability, complete interpretability, and near-zero CO2 emissions, effectively serving as the natural endpoint of contemporary modelling practice.

DBM Collaboration, David Komanek, Vaclav Pavlík, Santiago Jimenez, Rhys Taylor2026-04-01🔭 astro-ph

The Closure Challenge: a benchmark task for machine learning in turbulence modelling

This paper introduces "The Closure Challenge," a standardized open-source benchmark comprising curated datasets, evaluation code, and specific test cases designed to establish a common metric for evaluating and advancing machine learning models in Reynolds-averaged Navier-Stokes turbulence modeling.

Ryley McConkey, Tyler Buchanan, Tess Smidt, Abigail Bodner, Richard Dwight, Paola Cinnella2026-04-01🔬 physics

AI Cosplaying as Astrophysicists: A Controlled Synthetic-Agent Study of AI-Assisted Astrophysical Research Workflows

This study employs a controlled simulation of 144 synthetic astrophysicists to demonstrate that the efficacy of AI assistance in astrophysical research is highly conditional, varying significantly based on the specific task, the usage policy, and the underlying large language model, with some configurations improving workflow while others introduce catastrophic errors in derivation-heavy work.

Chun Huang2026-04-01🔭 astro-ph

A systematic approach to Covariance matrix formulation in charged particle activation experiments

This paper presents a systematic framework for constructing covariance and correlation matrices in charged particle activation experiments by explicitly calculating both statistical and systematic uncertainties through sensitivity coefficients and parameter propagation, thereby demonstrating the critical importance of accounting for correlated uncertainties in the interpretation and comparison of experimental cross-section data.

Tanmoy Bar2026-04-01✓ Author reviewed ⓘ⚛️ nucl-ex

FcsIT: An Open-Source, Cross-Platform Tool for Correlation and Analysis of Fluorescence Correlation Spectroscopy Data

FcsIT is a new, open-source, cross-platform Python tool that utilizes the Dear PyGUI engine to provide robust, user-friendly correlation, filtering, and flexible model-fitting capabilities for analyzing Fluorescence Correlation Spectroscopy data with quality comparable to commercial software.

Tomasz Kalwarczyk2026-04-01🧬 q-bio

Growth-rate distributions at stationarity

This paper introduces new analytical tools demonstrating that deviations from normality in stationary growth-rate distributions are natural statistical outcomes rather than anomalies, proposing a generalized logistic distribution as a robust null model and a practical workflow for identifying macroecological patterns in systems with limited data quality.

Edgardo Brigatti2026-04-01🧬 q-bio

Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network

This paper presents a Bayesian physics-informed neural network approach for flow field tomography that leverages governing fluid dynamics equations to reconstruct 2D flows from sparse line-of-sight measurements while providing comprehensive uncertainty quantification to address semi-convergence issues caused by noise.

Joseph P. Molnar, Samuel J. Grauer2026-03-31🔬 physics

Estimating density, velocity, and pressure fields in supersonic flow using physics-informed BOS

This paper introduces a novel physics-informed background-oriented schlieren (BOS) workflow that utilizes physics-informed neural networks to simultaneously reconstruct accurate density, velocity, and pressure fields in supersonic flows by integrating measurement data with governing Euler and irrotationality equations, thereby overcoming the limitations of conventional methods and achieving the first PINN-based reconstruction of supersonic flow from experimental data.

Joseph P. Molnar, Lakshmi Venkatakrishnan, Bryan E. Schmidt, Timothy A. Sipkens, Samuel J. Grauer2026-03-31🔬 physics

← Previous Next →