Results 171 to 180 of about 159,841 (265)

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems. [PDF]

Comput Vis ECCV
Alçalar YU, Akçakaya M.
europepmc   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Entropy-Regularized Iterative Weighted Shrinkage-Thresholding Algorithm (ERIWSTA) for inverse problems in imaging. [PDF]

PLoS One
Ma L, Wu B, Yao Y, Teng Y.
europepmc   +1 more source

Physics‐Constrained Constitutive Learning of Rate‐Limiting Timescales for Efficient Hydrogen‐Based Direct Reduction for Green Steel Making

Advanced Science, EarlyView.
A conversion‐resolved constitutive framework is developed for the hydrogen‐based direct reduction of iron oxide pellets. Effective reaction and transport timescales are inferred directly from measured trajectories and mapped against operating conditions, pellet architecture, and composition. The analysis reveals how late‐stage transport control emerges
Anurag Bajpai, Barak Ratzker, Pasquale Cavaliere, Dierk Raabe   +3 more
wiley   +1 more source

Multi-Scale Energy (MuSE) framework for inverse problems in imaging. [PDF]

IEEE Trans Comput Imaging
Chand JR, Jacob M.
europepmc   +1 more source

Enhanced Stability in Zero‐Excess Li‐Metal Batteries via Prelithiated Carbon Nanofiber Interlayers

Advanced Science, EarlyView.
Carbon nanofibers carbonized at 700°C show Li metal deposition predominantly on the side facing the Cu current collector. During delithiation, Li agglomerates remain as inactive lithium on the fiber surface. This behavior can be changed by a modified n‐Buli prelithiation without a washing step, suppressing dead Li formation and therefore enabling ...
Sandro Schöner, Marius Ast, Vera Michaela Barysch, Rebecca Erkes, Jule Meier‐Merziger, Pengfei Cao, Joachim Mayer, Josef Granwehr, Fabian Jeschull, Hermann Tempel, Shicheng Yu, Rüdiger‐A. Eichel   +11 more
wiley   +1 more source

Quantitative photoacoustic tomography: modeling and inverse problems. [PDF]

J Biomed Opt
Tarvainen T, Cox B.
europepmc   +1 more source

A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces

Advanced Science, EarlyView.
A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren, Hong Wang, Chak Wang Tse, Yi Zheng, Xi Chen, Wenqi Hu   +5 more
wiley   +1 more source