Xiancheng Zhu | Searcharxiv

arXiv2026

Machine learning for the design and prediction of soft-magnetic electromagnetic shielding FeCo-based alloys in laser cladding

Electromagnetic shielding materials play a pivotal role in both aerospace applications and daily life. However, their design and manufacturing still face persistent challenges. Machine learning demonstrates significant potential in accelerating material development and compositions optimization. Furthermore, laser additive manufacturing provides powerful technical support for fabricating multi-component, multifunctional electromagnetic shielding materials with tailored properties. In this study, the multiple machine learning strategies have been proposed, based on experimental derivation and soft magnetic material databases, to accelerate the design of multifunctional FeCo-based alloys for electromagnetic interference (EMI) shielding within an almost infinite compositional space. This work presents a novel approach for the rapid and automated discovery of multifunctional alloys with optimized EMI shielding effectiveness, as well as enhanced magnetic and electrical properties.

cond-mat.mtrl-sci↗

arXiv2025

Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution

Recently, Mamba-based methods, with its advantage in long-range information modeling and linear complexity, have shown great potential in optimizing both computational cost and performance of light field image super-resolution (LFSR). However, current multi-directional scanning strategies lead to inefficient and redundant feature extraction when applied to complex LF data. To overcome this challenge, we propose a Subspace Simple Scanning (Sub-SS) strategy, based on which we design the Subspace Simple Mamba Block (SSMB) to achieve more efficient and precise feature extraction. Furthermore, we propose a dual-stage modeling strategy to address the limitation of state space in preserving spatial-angular and disparity information, thereby enabling a more comprehensive exploration of non-local spatial-angular correlations. Specifically, in stage I, we introduce the Spatial-Angular Residual Subspace Mamba Block (SA-RSMB) for shallow spatial-angular feature extraction; in stage II, we use a dual-branch parallel structure combining the Epipolar Plane Mamba Block (EPMB) and Epipolar Plane Transformer Block (EPTB) for deep epipolar feature refinement. Building upon meticulously designed modules and strategies, we introduce a hybrid Mamba-Transformer framework, termed LFMT. LFMT integrates the strengths of Mamba and Transformer models for LFSR, enabling comprehensive information exploration across spatial, angular, and epipolar-plane domains. Experimental results demonstrate that LFMT significantly outperforms current state-of-the-art methods in LFSR, achieving substantial improvements in performance while maintaining low computational complexity on both real-word and synthetic LF datasets.

cs.CV↗