Results 61 to 70 of about 245 (199)
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.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
Zhejiang Daxue xuebao. Lixue ban, 2018 Based on the theory of local fractional calculus on fractal sets,the author established an identity involving local fractional integrals. Using the identity, some generalized Ostrowski type inequalities for generalized harmonically s-convex functions ...
SUNWenbing(孙文兵)
doaj +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.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
Janowski harmonic close-to-convex functions
International Journal of Mathematical Analysis, 2014 A harmonic mapping in the open unit disc D{double-struck} = {z||z| < 1} onto domain Ω* ⊂ ℂ is a complex valued harmonic function w = f(z) which maps D{double-struck} univalently Ω*. Each such mapping has a canonical representation f(z) = h(z) + g(z), where h(z) and g(z) are analytic in D{double-struck} and h(0) = g(0) = 0, and are called analytic part ...
Turhan, N. +2 more
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A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
Programmable Flocks: Hierarchical Swarm Control via Dynamic Parameter Injection
Advanced Intelligent Systems, EarlyView.We present a method for the control of robot swarms which allows the shaping and the translation of patterns of simple robots (“smart particles”), using two types of devices. These two types represent a hierarchy: a larger group of simple, oblivious robots (which we call the workers) that is governed by simple local attraction forces and a smaller ...
Vivek Shankar Varadharajan +1 more
wiley +1 more source
Convexity at infinity and bounded harmonic functions [PDF]
Bulletin of the Australian Mathematical Society, 1997 It is shown that a complete simply connected negatively curved manifold supports nontrivial bounded harmonic functions if the singular set of the ideal boundary is disconnected.
openaire +2 more sources
Advanced Intelligent Systems, EarlyView.A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng +4 more
wiley +1 more source
The Anatomical Record, EarlyView.Abstract The human mandibular symphysis concentrates multiaxial loads during function and remodels throughout growth, but the precise mechanisms underlying cortical bone shape during growth remain relatively unexplored. Approaches based solely on thickness or external cortical contours provide only partial insights and do not capture the functional ...
Ana Ribeiro +3 more
wiley +1 more source