Results 151 to 160 of about 50,968 (311)

Resolving the multitude of microscale interactions accurately models stochastic partial differential equations

, 2006
Constructing numerical models of noisy partial differential equations is a very delicate task. Our long-term aim is to use modern dynamical systems theory to derive discretisations of dissipative stochastic partial differential equations.
Roberts, A., Roberts, A. J.
core   +1 more source

Designable van der Waals Crystal for Artificial Neuronal Cell Mimicking

Advanced Materials, EarlyView.
Designable van der Waals crystal has been demonstrated for device‐scale neuronal cell mimicking. The structural similarity between ion‐channel in biological membranes and layered vdW lattices is realized with nano‐crystallization via Ar + H2S plasma sulfurization.
Jinhyoung Lee, Gunhyoung Kim, Dongho Lee, Seowoo Son, Hyunho Seok, Sihoon Son, Hyunbin Choi, Geonwook Kim, Geumji Back, Hyunkyu Kim, Chaerin Park, Junmin Ahn, Seongyun Je, Chaeyoung Im, Junil Cho, Magdalena Grzeszczyk, Seongho Kim, Eunseo Go, Hyunwoo Shim, Donghwan Choi, Muyoung Kim, Hyoeng‐U. Kim, Won‐Jun Jang, Taesung Kim   +23 more
wiley   +1 more source

Projection schemes for stochastic partial differential equations

, 2009
The focus of the present work is to develop stochastic reduced basis methods (SRBMs) for solving partial differential equations (PDEs) defined on random domains and nonlinear stochastic PDEs (SPDEs).
Prerapa, Surya Mohan
core  

Super‐Resolution Ultrasound Based Cell Tracking With Polymeric Nanobubbles

Advanced Materials, EarlyView.
This study presents a super‐resolution ultrasound platform for tracking cells in vivo. Biocompatible polymeric nanobubbles are used as highly echogenic intracellular labels. Following the injection of cells and microbubbles, ultrasound localization microscopy (ULM) can dynamically match the microvascular architecture and individual cell trajectories ...
Junlin Chen, Xiaoyu Wang, Jilin Fan, Bi Wang, Hanghang Fang, Yurui Wang, Hao Cui, Mohammad Roufarshbaf, Ekaterina Savina, Alexandra Valeske, Quim Peña, Yang Shi, Andreas Herrmann, Twan Lammers, Mathias Hornef, Roman Barmin, Thomas Lisson, Georg Schmitz, Anne Rix, Fabian Kiessling   +19 more
wiley   +1 more source

L p -Solutions and Comparison Results for Lévy-Driven Backward Stochastic Differential Equations in a Monotonic, General Growth Setting. [PDF]

J Theor Probab, 2022
Kremsner S, Steinicke A.
europepmc   +1 more source

A Survey of Interlayer Interaction Models for Graphene and Other 2D Materials

Advanced Materials Interfaces, EarlyView.
Van der Waals interactions arising from electronic polarization at atomically close interfaces generate corrugated interlayer energy landscapes that govern normal and tangential tractions. This review presents an overview of quantum, atomistic, analytical, and continuum modeling approaches, highlighting their roles across length scales in capturing ...
Gourav Yadav, Shakti S. Gupta, Roger A. Sauer   +2 more
wiley   +1 more source

Linear-implicit strong schemes for Itô-Galkerin approximations of stochastic PDEs

, 2010
Linear-implicit versions of strong Taylor numerical schemes for finite dimensional Itô stochastic differential equations (SDEs) are shown to have the same order as the original scheme.
Shott, Stephen, Kloeden, Peter E.
core   +1 more source

A 3D‐Printed Blister Test Platform for Quantifying Biointerface Adhesion Mechanisms

Advanced Materials Interfaces, EarlyView.
A 3D‐printed blister platform enables energy‐resolved characterization of soft hydrogel–rigid interfaces. Integrating precision pressure control with hyperelastic modeling directly quantifies adhesion energy (G) and R‐curve toughening. Results reveal that modulating hydrogel concentration and surface roughness drives a tunable transition from cohesive ...
Yoontae Kim, Rahul Patil, Argyris K. Lazaris, Xinran Zhang, Stella Alimperti   +4 more
wiley   +1 more source

Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization

Advanced Materials Technologies, EarlyView.
An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue, Yuan Gao, Fanping Sui, Jason Wang, Megan Teng, Peggy Tsao, Huicong Deng, Yande Peng, Liwei Lin   +8 more
wiley   +1 more source

Benchmarking Coaxial and Angular Optical Emission Spectroscopy With Recommendations for Reliable Compositional In Situ Monitoring During Laser Powder Bed Fusion

Advanced Materials Technologies, EarlyView.
ABSTRACT Real‐time insight into local chemistry is critical for reliable part quality in additive manufacturing, especially laser powder bed fusion (PBF‑LB/M), where rapid thermal cycles and localized evaporation can undermine part performance. Optical emission spectroscopy (OES) offers non‑intrusive, in situ plume monitoring, but detection geometry ...
Philipp Gabriel, Bastian Jülich, Anna Rosa Ziefuss, Florian Eibl, Stephan Barcikowski   +4 more
wiley   +1 more source