Results 91 to 100 of about 53,984 (288)

opXRD: Open Experimental Powder X‐Ray Diffraction Database

Advanced Intelligent Discovery, EarlyView.
We introduce the Open Experimental Powder X‐ray Diffraction Database, the largest openly accessible collection of experimental powder diffractograms, comprising over 92,000 patterns collected across diverse material classes and experimental setups. Our ongoing effort aims to guide machine learning research toward fully automated analysis of pXRD data ...
Daniel Hollarek, Henrik Schopmans, Jona Östreicher, Jonas Teufel, Bin Cao, Adie Alwen, Simon Schweidler, Mriganka Singh, Tim Kodalle, Hanlin Hu, Gregoire Heymans, Maged Abdelsamie, Arthur Hardiagon, Alexander Wieczorek, Siarhei Zhuk, Ruth Schwaiger, Sebastian Siol, François‐Xavier Coudert, Moritz Wolf, Carolin M. Sutter‐Fella, Ben Breitung, Andrea M. Hodge, Tong‐yi Zhang, Pascal Friederich   +23 more
wiley   +1 more source

Smooth planar $r$-splines of degree $2r$

, 2006
In \cite{as}, Alfeld and Schumaker give a formula for the dimension of the space of piecewise polynomial functions (splines) of degree $d$ and smoothness $r$ on a generic triangulation of a planar simplicial complex $\Delta$ (for $d \ge 3r+1$) and any ...
Alfeld, Alfeld, Billera, Deng, Diener, Schenck, Schenck, Schenck, Schenck, Ştefan O. Tohaˇneanu   +9 more
core   +1 more source

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

Modeling of Structure of the Specialized Processor on the Basis Ryabenko's Splines for Signal Processing

Journal of information and communication convergence engineering, 2011
The paper is devoted to problem of spline approximation. A new method of nodes location for curves and surfaces computer construction by means of B-splines, of Reyabenko"s splines and results of simulink-modeling is presented. The advantages of this paper is that we comprise the basic spline with classical polynomials both on accuracy, as well as ...
Hakimjon Zaynidinov, Golibjon Nishonboev
openaire   +2 more sources

More Results on B-Splines via Recurrence Relations [PDF]

, 1992
The present paper is to be understood as a revision and continuation of a recent series of publications on recursively defined B-splines, due to C. deBoor and K. Höllig [4] and G. Ciascola [6, 7].
Meinardus, Günter, Walz, Guido
core   +1 more source

Extending Battery Usage Time of a Heavy‐Duty Mecanum‐Wheeled Autonomous Electric Vehicle Used in Iron–Steel Industry by Considering Wheel Slippage

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar, Goktug Hambarci, Ali Sezai Demirel   +2 more
wiley   +1 more source

Exponential Splines and Pseudo-Splines: Generation versus reproduction of exponential polynomials

, 2014
Subdivision schemes are iterative methods for the design of smooth curves and surfaces. Any linear subdivision scheme can be identified by a sequence of Laurent polynomials, also called subdivision symbols, which describe the linear rules determining ...
Conti, Costanza, Gemignani, Luca, Romani, Lucia   +2 more
core  

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks

Advanced Intelligent Systems, EarlyView.
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan, Oliver Artner, Paul‐Philipp Manea, Sebastian Siegel, Susanne Hoffmann‐Eifert, Regina Dittmann, John Paul Strachan   +6 more
wiley   +1 more source

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models

BMC Medical Research Methodology, 2011
Background When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can
Eloranta Sandra, Dickman Paul W, Andersson Therese ML, Lambert Paul C   +3 more
doaj   +1 more source