A new study provides a rigorous theoretical and numerical analysis of the accuracy of the method of characteristics (MoC), a ...

The numerical integration of stiff equations is a challenging problem that needs to be approached by specialized numerical methods. Exponential integrators form a popular class of such methods since ...

Abstract: Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constant. TV ...

The most comprehensive, family-based variant dataset ever published will improve variant classification using AI-based tools MENLO PARK, Calif., Aug. 04, 2025 (GLOBE NEWSWIRE) -- PacBio (NASDAQ: PACB) ...

Dynamical low-rank approximation (DLRA) methods have emerged as a powerful numerical framework for addressing the challenges posed by high-dimensional problems. By restricting the evolution of a ...

Ultra-accurate simulations of the gravity field guide toward a new mathematical foundation of gravity modelling. A layered Earth. (Courtesy: iStock/AlexLMX) The Earth is not a perfect sphere. This ...

Abstract: Learning-based methods have been widely applied to solve electromagnetic (EM) inverse scattering problems (ISPs). In learning-based induced current inversions, the deterministic part of the ...

Let $P(m, X, N)$ be an $m$-degree polynomial in $X\in\mathbb{R}$ having fixed non-negative integers $m$ and $N$. Essentially, the polynomial $P(m, X, N)$ is a result ...

AI companies face delays and challenges with training new large language models Some researchers are focusing on more time for inference in new models Shift could impact AI arms race for resources ...

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. Pollsters trying to predict presidential election results ...