Results 21 to 30 of about 12,385,044 (225)

The geometry of visual experience [PDF]

, 2007
In this thesis I examine what the geometry of visual experience is. This is a question which to some has had an a priori answer, and to some its relevance to philosophy has been considered questionable.
Meadows, Phillip
core  

Accelerated Discovery of Topological Spin Phase Diagrams in CrSBr Magnets Via High‐Throughput Optimization

Advanced Functional Materials, EarlyView.
Here, we develop a high‐throughput algorithm that accelerates the elucidation of phase diagrams of topological spin textures using limited computational resources at high numerical accuracy. Applying this framework to the van der Waals magnet CrSBr, we unveiled a hierarchy of previously unknown topological textures (domain‐wall bimerons, bimeron chains,
Andrew Lyall, Mohammad H. Badarneh, Cristian Bonato, Rebecca Cheung, Elton J. G. Santos   +4 more
wiley   +1 more source

A Unified Superelliptic Framework for the Differential Geometry of Gielis Transformations

Axioms
The Gielis superformula is a powerful parametric tool that generates an infinite variety of natural and organic curves and surfaces through a compact set of parameters. However, classical differential geometry has lacked a unified framework for analyzing
Zehra Özdemir, Esra Parlak, Johan Gielis   +2 more
doaj   +1 more source

Full‐Field Damage Monitoring in Architected Lattices Using In situ Electrical Impedance Tomography

Advanced Functional Materials, EarlyView.
In situ electrical impedance tomography (EIT) turns 3D‐printed, CNT‐infused architected lattices into full‐field damage‐imaging systems. Tunable Voronoi‐based geometries act as active sensing architectures, enabling conductivity maps to detect early‐stage damage and localise sequential ligament fracture before catastrophic failure.
Akash Deep, Andrea Samore, Alistair McEwan, Andrew McBride, Shanmugam Kumar   +4 more
wiley   +1 more source

Shape operator of an $ (n-1) $-dimensional distribution on an $ n $-dimensional manifold and their classification [PDF]

ریاضی و جامعه
This paper aims to study of shape operator of an $ (n-1) $-dimensional distribution on an $ n $-dimensional smooth manifold. In this study firstly we state formulae for the shape operator and its symmetric and anti-symmetric components and in ...
Mehran Aminian, Mehran Namjoo
doaj   +1 more source

DID LOBACHEVSKY HAVE A MODEL OF HIS "IMAGINARY GEOMETRY"? [PDF]

, 2010
The invention of non-Euclidean geometries is often seen through the optics of Hilbertian formal axiomatic method developed later in the 19th century. However such an anachronistic approach fails to provide a sound reading of Lobachevsky's geometrical ...
Rodin, Andrei
core  

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

Topics in Non-Euclidean geometry [PDF]

, 1959
Thesis (M.A.)-- University of Wichita, College of Liberal Arts and Sciences, Dept. of MathematicsIt is difficult for a high school student to fully appreciate the study of Euclidean geometry without some notion of its relation to other geometries and ...
Carter, Martin
core   +2 more sources

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source

Geometric neural operators (gnps) for data-driven deep learning in non-euclidean settings

Machine Learning: Science and Technology
We introduce Geometric Neural Operators (GNPs) for data-driven deep learning of geometric features for tasks in non-euclidean settings. We present a formulation for accounting for geometric contributions along with practical neural network architectures ...
B Quackenbush, P J Atzberger
doaj   +1 more source