Results 121 to 130 of about 4,900,567 (209)
Hybrid physics-informed artificial intelligence for high-fidelity modeling and optimization of electrical systems. [PDF]
Nyangon J.
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
Fourier Neural Operators for Fast Multi-Physics Sensor Response Prediction: Applications in Thermal, Acoustic, and Flow Measurement Systems. [PDF]
Sayghe A, Mousa M, Batiyah S, Husawi A.
europepmc +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
BFMGHC: a boosted fuzzy manifold granule hypersurface classifier with local topology preservation. [PDF]
Li W, Si W, Liu Z, Song Y.
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Learning the reaction coordinate: collective variables from physical intuition to generative models.
Talmazan RA +5 more
europepmc +1 more source
Adaptive Multi-Fidelity Structural Optimization under Fluid-Structure Interaction. [PDF]
Narkhede A, Rivas E, Wang K.
europepmc +1 more source
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source

