Physics-informed AI extracts concrete fracture parameters from stress images
A hybrid physics-informed neural network extracts concrete fracture parameters from stress images with an error margin of about 3.9 percent.
A hybrid physics-informed neural network extracts concrete fracture parameters from stress images with an error margin of about 3.9 percent.
Machine learning models trained on finite element simulations predicted the photoionization cross section of a donor impurity in a GaAs/AlGaAs ...
Researchers have released bryne, an open-source Python framework built on DUNE-FEM that turns rapid finite element prototypes into reusable, metadata-rich ...
A new semi-analytical study combines Akbari-Ganji's method with finite element simulation to show how magnetic fields, buoyancy, activation energy and ...
A computational study finds that increasing damping shifts viscoelastically supported beam systems toward vibration suppression, while stiffer foundations raise natural ...
Researchers have doubled the speed and accuracy of mantle simulations by reformulating a decades-old iterative algorithm with viscosity-weighted error measurement ...
Machining trapezoidal grooves into copper before cold rolling creates tunable gradient microstructures, with simulations and experiments revealing how groove geometry ...
A new study of a 40-meter highway slope in Southwest China shows how saturated clay seams trigger stepped sliding failure, ...
A new editorial in Frontiers in Manufacturing Technology surveys how finite element methods are transforming precision manufacturing, from laser shock ...
An open-source MATLAB toolbox called LeClapotis uses the finite element method to compute natural sloshing frequencies for fuel tanks of ...
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© 2025 Scienmag - Science Magazine