Iterative convergence in phase-field brittle fracture computations: exact line search is all you need. [PDF]
Heinzmann J +3 more
europepmc +1 more source
Abstract The sudden and localized nature of thunderstorm gusts makes pure data‐driven models struggle to maintain physical plausibility and interpretability, leading to nonphysical false alarms and spatial displacement in complex terrain. To address this, we propose PhyTGNet, a deep learning framework with embedded physics‐inspired guidance that ...
Zhaoyu Yan +5 more
wiley +1 more source
Global Existence and Nonlinear Stability of Finite-Energy Solutions of the Compressible Euler-Riesz Equations with Large Initial Data of Spherical Symmetry. [PDF]
Carrillo JA +3 more
europepmc +1 more source
Calibration Method of Empirical Atmospheric Density Based on Physics‐Informed Neural Networks
Abstract Thermospheric density is a critical parameter for low Earth orbit satellite applications, including orbit prediction, collision risk assessment, and atmospheric drag analysis. However, empirical atmospheric density models exhibit significant uncertainties under disturbed space weather conditions, while purely data‐driven approaches may not ...
Weirong Huang +6 more
wiley +1 more source
A Generalized Unit Hydrograph Theory for Dynamic River Networks
Abstract The instantaneous unit hydrograph (IUH) synthetically describes how effective rainfall is transformed into discharge at the catchment outlet. According to classical geomorphological theories, the IUH is regarded as a time‐invariant probability density function of watershed response times, an interpretation that implicitly postulates a fixed ...
Gianluca Botter +4 more
wiley +1 more source
From PINNs to PIKANs: recent advances in physics-informed machine learning. [PDF]
Toscano JD +6 more
europepmc +1 more source
An EEG‐based BCI was developed to train self‐regulation of the theta/delta power ratio during natural speech listening in adults with developmental dyslexia. Participants controlled a spaceship through their neural activity across eight daily training sessions.
Xuanci Zheng +3 more
wiley +1 more source
Quantum-informed machine learning for predicting spatiotemporal chaos with practical quantum advantage. [PDF]
Wang M, Xue X, Gao M, Coveney PV.
europepmc +1 more source
Random Carbon Tax Policy and Investment Into Emission Abatement Technologies
ABSTRACT We analyze the problem of a profit‐maximizing electricity producer, subject to carbon taxes, who decides on investments into CO2$\rm CO_2$ abatement technologies. We assume that the carbon tax policy is random and that the investment in the abatement technology is divisible, irreversible, and subject to transaction costs.
Katia Colaneri +2 more
wiley +1 more source
Virtual cell: Current perspectives and future prospects. [PDF]
Li J +5 more
europepmc +1 more source

