Results 41 to 50 of about 5,568 (215)
Embeddings of compact convex sets and locally compact cones [PDF]
The main result of this paper is that a compact convex set with a basis of neighborhoods (not necessarily open) at each point which are convex can be embedded in a locally convex separated topological vector space.
Lawson, Jimmie D.
core +2 more sources
Generalized hyperbolicity, stability and expansivity for operators on locally convex spaces [PDF]
We introduce and study the notions of (generalized) hyperbolicity, topological stability and (uniform) topological expansivity for operators on locally convex spaces.
N. Bernardes +4 more
semanticscholar +1 more source
Topological properties and matrix transformations of certain ordered generalized sequence spaces
In this note, we carry out investigations related to the mixed impact of ordering and topological structure of a locally convex solid Riesz space (X,τ) and a scalar valued sequence space λ, on the vector valued sequence space λ(X) which is formed and ...
Manjul Gupta, Kalika Kaushal
doaj +1 more source
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
A formula to calculate the spectral radius of a compact linear operator
There is a formula (Gelfand's formula) to find the spectral radius of a linear operator defined on a Banach space. That formula does not apply even in normed spaces which are not complete.
Fernando Garibay Bonales +1 more
doaj +1 more source
Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization
Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process ...
Yeong Woo Son +4 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
Internal functionals and bundle duals
If π:E→X is a bundle of Banach spaces, X compact Hausdorff, a fibered space π*:E*→X can be constructed whose stalks are the duals of the stalks of the given bundle and whose sections can be identified with the functionals studied by Seda in [1] and [2]
Joseph W. Kitchen, David A. Robbins
doaj +1 more source
Holomorphic functions on locally convex topological vector spaces. II. Pseudo convex domains [PDF]
In this article we discuss the relationship between domains of existence domains of holomorphy, holomorphically convex domains, pseudo convex domains, in the context of locally convex topological vector spaces. By using the method of Hirschowitz for
openaire +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

