324 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 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.
The paper introduces AUSSIE, a novel method for correcting LHC detector effects that achieves asymptotically correct unfolding without relying on iterations, adversarial training, or surrogate forward mappings by employing a new loss function to minimize dependence on reference simulations.
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 paper applies fluid dynamics theories to temporal networks by introducing two eigendecomposition methods—Proper Orthogonal Decomposition for network compression and reconstruction, and a Koopman operator approximation for spectral analysis of dynamic modes—both of which are validated using synthetic generative models.
Using a model-independent Gaussian Process reconstruction of cosmic expansion history from cosmic chronometer data, this study tests the constancy of Type Ia supernova luminosities and finds them generally consistent with standard candles within 1σ, while identifying localized, non-statistical deviations in both Pantheon+ and DES 5YR datasets that suggest a possible non-monotonic luminosity evolution driven by varying physical mechanisms across different redshifts.
This paper introduces a novel maximum likelihood estimation framework utilizing sparse optimization and an iteratively reweighted least squares algorithm to accurately track particles in turbulent flows, effectively recovering heavy-tailed acceleration statistics and outperforming existing Gaussian-based methods by preserving the physical intermittency inherent in high-Reynolds-number turbulence.
This paper warns that hybrid metrology for semiconductor manufacturing risks catastrophic failure due to "dark uncertainty" caused by inconsistent measurement results, arguing that standard statistical models dangerously underestimate total uncertainty while random effects models offer a more robust approach to safely combine indeterminately consistent data.
This paper demonstrates that coverage-oriented uncertainty quantification methods, which integrate uncertainty directly into the optimization process, outperform post-hoc techniques in modeling the complex, multi-regime physical behaviors of Critical Heat Flux by producing models with both high predictive accuracy and physically consistent uncertainty estimates.
This paper presents a Bayesian framework that leverages historical network snapshots to establish informative priors for predicting future network configurations and quantifying uncertainty, demonstrating superior out-of-sample reconstruction accuracy on interbank market data compared to existing link prediction benchmarks.