Results 141 to 150 of about 125,930,784 (300)
dynoGP: Deep Gaussian Processes for Dynamic System Identification
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora +4 more
wiley +1 more source
This study examines how helicoidal architectures with different porosities respond to bending. Adjusting layer angle and spacing in 3D‐printed polymers reveals clear tradeoffs between stiffness, strength, and energy absorption. Experiments and simulations highlight designs that distribute stress effectively, offering pathways for optimizing lightweight
Praveenkumar Subhash Patil +2 more
wiley +1 more source
Copper‐based composites enhanced with carbon feature convenient mechanical properties and favorable electric conductivity. Processing via deformation and thermomechanical treatments can introduce advantageous microstructures further enhancing their performance. Herein, copper–graphene powder‐based composites are directly consolidated via rotary swaging
Radim Kocich +3 more
wiley +1 more source

