Results 31 to 40 of about 2,931 (225)
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
Based on the double nonlinear consolidation constitutive associated with the compression and permeability coefficients, presented by Mesri and Rokhsar (1974), this paper derives an approximate closed-form solution for the one-dimensional nonlinear ...
Zongqin Wang +6 more
doaj +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 article presents the design, modeling, and characterization of air‐pressure–actuated programmable vibroacoustic metamaterials (PVAMM). The study focuses on leveraging air pressure to dynamically tune resonance frequencies for effective noise attenuation.
William Kaal +2 more
wiley +1 more source
A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 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
One-dimensional large-strain thaw consolidation using nonlinear effective stress – void ratio – hydraulic conductivity relationships [PDF]
A one-dimensional model for the consolidation of thawing soils is formulated in terms of large-strain consolidation and heat-transfer equations. The model integrates heat transfer due to conduction, phase change, and advection. The hydromechanical behaviour is modelled by large-strain consolidation theory.
Dumais, Simon, Konrad, Jean-Marie
openaire +1 more source
Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier +17 more
wiley +1 more source
The study on soil consolidation and settlement is of great importance in geotechnical engineering practice. Nowadays, physics-informed neural networks (PINN) are becoming more and more popular in solving geotechnical engineering problems thanks to their ...
Jie Zhou, De’an Sun, Yang Chen
doaj +1 more source

