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 +8 more
wiley +1 more source
Advanced Science, EarlyView.ABSTRACT The muscle capsule of Trichinella is a critical structure that impedes immune attacks and drug penetration, yet the molecular mechanisms underlying its formation remain poorly understood. Using a high‐quality super‐pangenome comprising 12 Trichinella species, we compared extensive genomic variations between encapsulating and non‐encapsulating ...
Qingbo Lv +8 more
wiley +1 more source
ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
Advanced Science, EarlyView.The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray +3 more
wiley +1 more source
Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design
Advanced Science, EarlyView.ABSTRACT Replicating the sophisticated sense of touch in artificial systems requires tactile sensors with precisely tailored properties. However, manually navigating the complex microstructure‐property relationship results in inefficient and suboptimal designs.
Baocheng Wang +15 more
wiley +1 more source
Improved genomic prediction using machine learning with Variational Bayesian sparsity. [PDF]
Plant Methods, 2023 Yan Q +7 more
europepmc +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 +8 more
wiley +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 +5 more
wiley +1 more source
Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion. [PDF]
Entropy (Basel), 2023 Shekaramiz M, Moon TK.
europepmc +1 more source
V‐UNet: Medical Image Segmentation Based on Variational Attention Mechanism
CAAI Transactions on Intelligence TechnologyAccurate medical image segmentation plays a crucial role in improving the precision of computer‐aided diagnosis. However, complex boundary shapes, low contrast and blurred anatomical structures make fine‐grained segmentation a challenging task ...
Yang Zhang +6 more
doaj +1 more source
Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems
Advanced Electronic Materials, EarlyView.Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang +9 more
wiley +1 more source