Results 111 to 120 of about 49,141 (264)
Physics‐Aware Machine‐Learning‐Driven Inverse Design of Broadband Ultra‐Open Acoustic Metamaterials
A physics‐aware machine‐learning framework enables inverse design of ultra‐open acoustic silencers by decoupling spectral and radial design spaces. The approach rapidly identifies broadband, compact, and highly ventilated architectures, while revealing hidden linear design rules that link geometry, impedance matching, and acoustic performance.
Zhiwei Yang +5 more
wiley +1 more source
A Globally Optimal Alternative to MLP
In deep learning, achieving the global minimum poses a significant challenge, even for relatively simple architectures such as Multi-Layer Perceptrons (MLPs). To address this challenge, we visualized model states at both local and global optima, thereby identifying the factors that impede the transition of models from local to global minima when ...
Zheng Li, Jerry Cheng, Huanying Helen Gu
openaire +1 more source
A latent diffusion‐based framework is proposed for designing functionally graded metamaterials with perfect connectivity. By integrating vector‐quantized latent representations with mechanistic guidance, the framework enables accurate inverse design toward target elastic properties.
Jongbin Yu, Dosung Lee, Namjung Kim
wiley +1 more source
CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model
In this work, CoSP (contrastive multi‐state pretrain), an intelligent inverse design method for reconfigurable metamaterials based on a contrastive pretrained large language model, is proposed. Numerical experiments demonstrate that CoSP can design reconfigurable metamaterial structures for multi‐state, multi‐band optical responses, showing great ...
Shujie Yang +4 more
wiley +1 more source
EndoTac presents a trocar‐compatible endoscopic vision‐based tactile sensor that uses a convex mirror to enlarge side‐facing tactile coverage during minimally invasive vessel palpation. Distorted tactile images are unwarped and processed by a learning model to estimate vascular deformation, enabling sensitive, spatially distributed tactile perception ...
Yupeng Wang +5 more
wiley +1 more source
TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao +8 more
wiley +1 more source
An attention‐based multimodal deep learning framework is developed to predict the creep life of Ni‐based superalloys by fusing processing parameters with microstructural micrographs. The model achieves high accuracy (R2 = 0.92), aligns with metallurgical principles by capturing δ‐phase influence, and incorporates uncertainty quantification, offering a ...
Haopeng Lv +10 more
wiley +1 more source
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao +6 more
wiley +1 more source
This study presents a comparative analysis of fault detection and classification systems developed for current sensors in a drive system with a permanent magnet synchronous motor (PMSM).
Jankowska Kamila Anna
doaj +1 more source

