Results 81 to 90 of about 250,699 (239)
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +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
POTENTIAL REDUCTION METHOD FOR HARMONICALLY CONVEX-PROGRAMMING
In this paper, we introduce a potential reduction method for harmonically convex programming. We show that, if the objective function and the m constraint functions are all k-harmonically convex in the feasible set, then the number of iterations needed ...
Zhang, S (Shuzhong), ZHANG, S, STURM, JF
core +1 more source
Two-Dimensional Operator Harmonically Convex Functions and Related Generalized Inequalities
In this study, we studied on the harmonically convexity of functions. Firstly, we obtained some new generalized Hadamard’s type and Ostrowski’s type inequalities for these functions on the real number line.
Nurgül OKUR, Fatma Buğlem YALÇIN
core +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
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
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
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
In this paper, some Hermite-Hadamard type inequalities for products of two harmonically convex functions via Riemann-Liouville fractional integrals are established.
Kunt, Mehmet +2 more
core +1 more source
Some Inequalities of Jensen Type and Lazhar Type for the Class of Harmonically and Strongly Reciprocally Convex Functions [PDF]
In recent years, new classes of convex functions have been introduced in order to generalize the results and to obtain new estimations, in [12], Iscan gave the definition of harmonically convex functions and in [2] Bracamonte et al.
Medina, J. +5 more
core +1 more source
Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica +38 more
wiley +1 more source

