Results 131 to 140 of about 84,906 (335)
Analytic morphing of wurtzite nanowire cross sections to arbitrary shapes yields numbers of nanowire atoms, of bonds between these and of nanowire interface bonds, plus the nanowire cross section area. The ratios of above variables help to interpret any spectroscopic nanowire data which depend on diameter and cross section shape, and can be applied to ...
Dirk König, Sean C. Smith
wiley +1 more source
Crystal Structure Prediction of Cs–Te with Supervised Machine Learning
High‐throughput density functional theory calculations combined with machine learning models are employed to predict stable Cs– Te binary crystals. By systematically evaluating various structural descriptors and learning algorithms, the superiority of models based on atomic coordination environments is revealed.
Holger‐Dietrich Saßnick+1 more
wiley +1 more source
$T$-polynomial convexity and holomorphic convexity [PDF]
We compare the $T$-polynomial convexity of Guedj with holomorphic convexity away from the support of $T$. In particular we show an Oka--Weil theorem for $T$-polynomial convexity, as well as present a situation when the notions of $T$-polynomial convexity and holomorphic convexity of $X\setminus\text{Supp }T$ coincide in the context of complex ...
arxiv
Convex-transitivity and function spaces [PDF]
If X is a convex-transitive Banach space and 1\leq p\leq \infty then the closed linear span of the simple functions in the Bochner space L^{p}([0,1],X) is convex-transitive. If H is an infinite-dimensional Hilbert space and C_{0}(L) is convex-transitive, then C_{0}(L,H) is convex-transitive. Some new fairly concrete examples of convex-transitive spaces
arxiv
SyMO: A Hybrid Approach for Multi‐Objective Optimization of Crystal Growth Processes
The hybrid SyMO (Symbolic regression Multi‐objective Optimization) framework combines Computational Fluid Dynamics (CFD), machine learning, and mathematical optimization techniques to investigate the effects of various process parameters, furnace geometries, and radiation shield material properties on key crystal quality metrics in Czochralski silicon (
Milena Petkovic, Natasha Dropka
wiley +1 more source
Operator log-convex functions and f-divergence functional [PDF]
We present a characterization of operator log-convex functions by using positive linear mappings. Moreover, we study the non-commutative f-divergence functional of operator log-convex functions. In particular, we prove that f is operator log-convex if and only if the non-commutative f-divergence functional is operator log-convex in its first variable ...
arxiv
State of the Art of Low‐Frequency Acoustic Modulation: Intensity Enhancement and Directional Control
High intensity low‐frequency sound sources hold significant value in many fields. However, their long wavelength, strong penetrability, and tendency to diffract make direction control and intensity enhancement challenging. Acoustic generators and metamaterial‐based acoustic devices still face issues such as low energy efficiency, poor directional ...
Jingsong Xu+13 more
wiley +1 more source
Nearly convex sets: fine properties and domains or ranges of subdifferentials of convex functions [PDF]
Nearly convex sets play important roles in convex analysis, optimization and theory of monotone operators. We give a systematic study of nearly convex sets, and construct examples of subdifferentials of lower semicontinuous convex functions whose domain or ranges are nonconvex.
arxiv
High‐Fidelity Computational Microscopy via Feature‐Domain Phase Retrieval
An innovative phase retrieval framework, termed FD‐PR, is uniquely established in the image's feature domain through the feature‐extracted, physical‐driven regression with interfaces for combining physics and image processing constraints. FD‐PR takes advantage of invariance components of an image against presences of model mismatch and uncertainty ...
Shuhe Zhang+4 more
wiley +1 more source