Results 121 to 130 of about 289,279 (277)
Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials
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
This review examines the evolution of bioprinting toward minimally invasive in situ strategies for internal organ regeneration. It defines the technological roadmap from handheld systems to advanced minimally invasive bioprinting platforms, positioning soft robotics as a core enabler.
Duc Tu Vu +9 more
wiley +1 more source
Reservoir dams play a pivotal role in water resource management. Accurate prediction of inflow to reservoirs significantly enhances operational performance.
Elman Athari +2 more
doaj +1 more source
In this dissertation, we focus on improving bilevel optimization through several approaches developed during research. Bilevel optimization problems consist of upper-level and lower-level optimization problems connected hierarchically. Upper-level and lower-level problems are also referred to as the leader and the follower problems in the literature ...
openaire +1 more source
ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
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
Lung X-Ray Image Classification Using DenseNet-169 and Bayesian Optimization
The increasing prevalence of lung diseases caused by infections such as Pneumonia and COVID-19 highlights the urgent need for accurate and efficient early detection methods.
Fayza Shahira, Benny Sukma Negara
doaj +1 more source
Explainable Bayesian Optimization
Abstract Manual parameter tuning of cyber-physical systems is a common practice, but it is labor-intensive. Bayesian Optimization (BO) offers an automated alternative, yet its black-box nature reduces trust and limits human-BO collaborative system tuning. Experts struggle to interpret BO recommendations due to the lack of explanations.
Tanmay Chakraborty +2 more
openaire +2 more sources
This work establishes a pipeline that transforms fragmented literature into a structured database for graphitic carbon nitride photocatalyst discovery. A prompt‐engineered, cross‐model large language model ensemble automates high‐fidelity extraction, enabling interpretable machine learning to identify dominant performance descriptors. These data‐driven
Dianyuan Li +7 more
wiley +1 more source
Multi‐Physical Field Modulated P‐Bit Device Based on VO2 Thin Film
We have proposed a VO2‐based P‐bit device where synergistic multi‐physical field modulation enables real‐time tunability of randomness. Besides introducing a new phase‐change material‐based device approach for high‐performance P‐bits, this study also demonstrates a synergistic multi‐physical field modulation strategy that opens new opportunities for ...
Bowen Sun +10 more
wiley +1 more source
Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design
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

