Results 181 to 190 of about 27,824 (260)

Physics‐Informed Generative Machine Learning for Designing Crack‐Free γ′‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

open access: yesMaterials Genome Engineering Advances, EarlyView.
This research proposes a physics‐informed generative machine learning framework to design SHA800, a crack‐free γ′‐strengthened nickel‐based superalloy for laser powder bed fusion, achieving a 43% γ′ volume fraction and 587 HV0.2 hardness. ABSTRACT Fabricating γ′‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant ...
Kai Guo   +11 more
wiley   +1 more source

Symbolic Regression‐Guided Feature Engineering for Predicting Magnetization in Cu‐Based Alloys Under Data‐Scarce Conditions

open access: yesMaterials Genome Engineering Advances, EarlyView.
A symbolic regression approach (SISSO) with physics‐informed feature engineering achieves high‐accuracy prediction of magnetic properties in Cu‐based alloys under data‐scarce conditions. The framework offers an interpretable and transferable strategy for accelerated alloy design.
Buyang Ma   +6 more
wiley   +1 more source

DeepRelaxo: Fast Mono‐Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self‐Supervised Deep Learning

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose We introduce DeepRelaxo, a fast and generalizable deep learning method for estimating brain R2* maps from multi‐echo gradient echo (ME‐GRE) acquisitions with arbitrary echo configurations, including shortened echo trains for accelerated scans.
Samiha Prima   +3 more
wiley   +1 more source

Physics‐Guided Neural Network for Quantitative Parameter Mapping Using Balanced Steady State Free Precession MRI

open access: yesMagnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To propose a new method using a physics‐guided neural network for quantitative parameter mapping in balanced steady‐state free precession (bSSFP) imaging. Theory and Methods We trained physics‐guided neural networks with a multilayer perceptron using simulated bSSFP signals generated from tissue parameters (T1$$ {T}_1 $$, T2$$ {T}_2 $$,
Hye‐Ryeong Choi   +2 more
wiley   +1 more source

Experimental Comparative Analysis of Hole‐Making Strategies and Cutting Parameters on Flexural Properties and Induced Delamination in S2 Glass and Basalt Fiber‐Reinforced Polymers

open access: yesPolymer Composites, EarlyView.
Hole‐making strategy and machining parameters influence delamination and hole quality in S2‐glass and basalt FRP composites, which subsequently affet flexural performance. Improved hole integrity leads to enhanced structural performance during three‐point bending.
Sara Saeed Abdulrahman Eltahir   +2 more
wiley   +1 more source

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