Results 181 to 190 of about 5,565,025 (258)

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

Inverse Fluid Convection Problems in Enclosures

Journal of Applied Mathematics, 2012
Fu-Yun Zhao, Di Liu, Steve H. L. Yim
doaj   +1 more source

Correction: WF-PINNs: solving forward and inverse problems of burgers equation with steep gradients using weak-form physics-informed neural networks. [PDF]

Sci Rep
Wang X, Yi S, Gu H, Xu J, Xu W.
europepmc   +1 more source

Structural engineering from an inverse problems perspective. [PDF]

Proc Math Phys Eng Sci, 2022
Gallet A, Rigby S, Tallman TN, Kong X, Hajirasouliha I, Liew A, Liu D, Chen L, Hauptmann A, Smyl D.   +9 more
europepmc   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Generative modelling meets Bayesian inference: a new paradigm for inverse problems. [PDF]

Philos Trans A Math Phys Eng Sci
Oliviero-Durmus A, Janati Y, Moulines E, Pereyra M, Reich S.   +4 more
europepmc   +1 more source

Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami

Advanced Science, EarlyView.
This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning, Lu Lu, Sophie Leanza, Ruike Renee Zhao   +3 more
wiley   +1 more source

Denoising: a powerful building block for imaging, inverse problems and machine learning. [PDF]

Philos Trans A Math Phys Eng Sci
Milanfar P, Delbracio M.
europepmc   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Spot the bot: the inverse problems of NLP. [PDF]

PeerJ Comput Sci
Gromov VA, Dang QN, Kogan AS, Yerbolova A.   +3 more
europepmc   +1 more source