Results 81 to 90 of about 11,198,121 (288)
Machine learning in physics – philosophical perspectives on new tools
, 2022 In most fields of physics, machine learning (ML) is all the rage. Physicists use ML algorithms to analyze data, to cast predictions or to emulate computer simulations. But what exactly is machine learning? How trustworthy are its results?
Beisbart, Claus
core
Advanced Engineering Materials, EarlyView.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
Physics-informed Machine Learning and Its Research Prospects in GeoAI
, 2023 Scientists still cannot fully understand and explain many complex physical phenomena and dynamic systems, which cannot be described by deterministic mathematic equations and be analyzed and predicted through compact physical mechanistic models.
Wang, Peixiao +5 more
core +1 more source
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
Geophysical Research LettersIn 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here,
Rüdiger Brecht, Alex Bihlo
doaj +1 more source
Chemical Engineering Journal AdvancesA physics-informed machine learning (ML) model, which incorporates the conservation of carbon mass, was developed to predict the product gas yield and composition for indirect gasification of waste in a fluidized bed.
Surika van Wyk
doaj +1 more source
Transforming physics-informed machine learning to convex optimization
Engineering Applications of Artificial Intelligence33 pages,14 ...
Letian Yi +3 more
openaire +4 more sources
Advanced Engineering Materials, EarlyView.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
Wind Farm Modeling with Interpretable Physics-Informed Machine Learning
Energies, 2019 Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their ...
Michael F. Howland, John O. Dabiri
doaj +1 more source
Investigating the Low‐Temperature Phase Stability of the Binary Ta–W System
Advanced Engineering Materials, EarlyView.Atomistic simulations show that the binary Ta–W system forms ordered intermetallic phases, B2‐TaW and D03‐TaW3, as 0 K ground states. Configurational entropy, however, lowers the free energy of the disordered bcc solid solution, which becomes the stable phase above about 400 K.
Klemens Lechner +7 more
wiley +1 more source