316 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 addresses the challenge of inferring protein production kinetics in dividing cells by overcoming the intractability of standard likelihood methods caused by non-Markovian division history effects, utilizing conditional normalizing flows to reveal that the yeast *glc3* gene exhibits mostly inactive, brief, and transient expression under nutrient stress.
This paper introduces DeepX-GAN, a dependence-aware generative model that simulates unseen spatial heat extremes beyond historical records to reveal hidden risks and project shifting vulnerability hotspots in the Middle East and North Africa under future warming.
This article introduces a Special Issue on Symbolic Regression for the Physical Sciences, summarizing its conceptual foundations, diverse applications, methodological challenges, and future directions in uncovering interpretable mathematical relationships from data.
This paper establishes a theoretical framework linking linear response theory, pullback measures, and optimal fingerprinting to predict forcing impacts and attribute anomalies in nonautonomous, time-dependent systems, a capability demonstrated through numerical applications to a modified Ghil-Sellers energy balance model for climate change detection.
This paper introduces a physics-informed neural operator framework that robustly estimates frequency-dependent surface admittance of locally reacting sound absorbers directly from noisy near-field measurements by embedding governing acoustic equations into the training process, thereby enabling accurate in situ characterization without explicit forward modeling.
This study evaluates three analytical frameworks for quantifying injection-driven mass transfer in porous media using time-lapse X-ray micro-CT, introducing a volume-ratio filtering technique to mitigate dissolution-driven biases and demonstrating that while all methods yield comparable average mass transfer coefficients, the choice of approach ultimately depends on the trade-off between desired physical detail and available computational resources.
This paper utilizes diffusion theory to derive analytical solutions for Langevin stochastic differential equations modeling pulsar timing noise and gravitational wave backgrounds, revealing that while an Ornstein-Uhlenbeck process for spin frequency is inconsistent with stationary signals, an overdamped harmonic oscillator and a two-component neutron star model successfully describe stationary dynamics and explain the physical origins of nonstationarity.
This paper establishes the quantitative relationships between terms in multidimensional Langevin equations and statistical properties like moments and covariance functions, extending the analysis from linear Gaussian dynamics to underdamped and non-Markovian regimes to aid in the inference of Langevin dynamics from data.
This paper introduces an adaptive, symmetry-informed Bayesian metrology strategy that significantly enhances parameter estimation precision in quantum technology experiments, achieving a five-fold reduction in variance or a three-fold reduction in required data compared to standard methods.
This study introduces a Bayesian framework using simulation-based inference and neural networks to accurately estimate frequency-dependent acoustic surface impedances from sparse interior sound pressure measurements, overcoming the limitations of conventional methods and enabling robust, uncertainty-quantified characterization of complex real-world environments.