Results 91 to 100 of about 3,282,070 (234)

Reproducing Kernel Krein Spaces of Analytic Functions and Inverse Scattering [PDF]

, 1985
The purpose of this thesis is to study certain reproducing kernel Krein spaces of analytic functions, the relationships between these spaces and an inverse scattering problem associated with matrix valued functions of bounded type, and an operator model.
Alpay, Daniel
core   +1 more source

Making Indefinite Kernel Learning Practical [PDF]

In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems ...
Mierswa, Ingo
core  

Vector Valued Reproducing Kernel Hilbert Spaces Integrable, Functions and Mercer Theorem

, 2006
We characterize the reproducing kernel Hilbert spaces whose elements are p-integrable functions in terms of the boundedness of the integral operator whose kernel is the reproducing kernel.
DE VITO, Ernesto, CARMELI, CLAUDIO, A. Toigo, TOIGO, ALESSANDRO, C. Carmeli, E. De Vito   +5 more
core   +1 more source

Autonomous Multi‐Objective Nanoscale Characterization of Combinatorial (Al, Sc, B)N Films Reveals Composition‐Dependent Ferroelectric Regimes

Advanced Functional Materials, EarlyView.
Autonomous scanning probe microscopy and multi‐objective Bayesian optimization navigate a ternary (Al,Sc,B)N combinatorial library. Registered photoluminescence, electron‐probe compositional mapping, and X‐ray diffraction connect local electromechanical function to defect‐sensitive emission, composition, and crystal structure.
Yu Liu, Aditya Raghavan, Utkarsh Pratiush, Ian Mercer, Pochun Hsieh, Albert Suceava, Chih‐Yu Lee, Edgar Dimitrov, Mauricio Terrones, Ichiro Takeuchi, Venkatraman Gopalan, Jon‐Paul Maria, Sergei V. Kalinin   +12 more
wiley   +1 more source

XTRUDE: A Process‐Informed Framework for High‐Fidelity Analysis of Hydrogel Extrusion

Advanced Functional Materials, EarlyView.
Extrusion performance emerges from the interplay between material properties and processing conditions. XTRUDE (eXTrusion Rheology for Understanding and Defining Extrudability) recreates the extrusion environment with in situ pressure and temperature sensing, enabling characteristic pressure signatures to quantify extrusion‐specific phenomena and ...
Farhad Sanaei, Pascal Bertsch, Juliette Lafosse, Sander C. G. Leeuwenburgh, Mani Diba   +4 more
wiley   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

A New Method of Solving the Time-Fractional Mixed Nonlinear Diffusion and Diffusion-Wave Equation

Fractal and Fractional
It is well known that meshless methods are effective for solving fractional differential equations on both regular and irregular domains. However, many commonly used basis functions such as Legendre wavelets and B-splines are naturally defined on ...
Hong Du, Zhong Chen, Tiejun Yang
doaj   +1 more source

Functional models for Nevanlinna families [PDF]

Opuscula Mathematica, 2008
The class of Nevanlinna families consists of \(\mathbb{R}\)-symmetric holomorphic multivalued functions on \(\mathbb{C} \setminus \mathbb{R}\) with maximal dissipative (maximal accumulative) values on \(\mathbb{C}_{+}\) (\(\mathbb{C}_{-}\), respectively)
Jussi Behrndt, Seppo Hassi, Henk de Snoo
doaj  

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Distance Functions for Reproducing Kernel Hilbert Spaces

, 2011
Suppose $H$ is a space of functions on $X$. If $H$ is a Hilbert space with reproducing kernel then that structure of $H$ can be used to build distance functions on $X$. We describe some of those and their interpretations and interrelations.
R. Rochberg, E. Sawyer, B. Wick, ARCOZZI, NICOLA   +3 more
core   +1 more source