Zobrazeno 1 - 10
of 7 086
pro vyhledávání: '"A, Karttunen"'
Autor:
Glova, Artem, Karttunen, Mikko
Machine learning (ML) methods provide advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass transition
Externí odkaz:
http://arxiv.org/abs/2406.20018
We use lattice-Boltzmann molecular dynamics (LBMD) simulations to study the compression of a confined polymer immersed in a fluid and pushed by a large spherical colloid with a diameter comparable to the channel width. We examined the chain's deforma
Externí odkaz:
http://arxiv.org/abs/2406.14741
Publikováno v:
Journal of Business & Industrial Marketing, 2024, Vol. 39, Issue 13, pp. 128-144.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JBIM-09-2023-0542
Electromagnetically propelled helical nanoswimmers offer great potential for nanorobotic applications. Here, the effect of confinement on their propulsion is characterized using lattice-Boltzmann simulations. Two principal mechanisms give rise to the
Externí odkaz:
http://arxiv.org/abs/2311.00839
Autor:
Kiyani, Elham, Kooshkbaghi, Mahdi, Shukla, Khemraj, Koneru, Rahul Babu, Li, Zhen, Bravo, Luis, Ghoshal, Anindya, Karniadakis, George Em, Karttunen, Mikko
The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature
Externí odkaz:
http://arxiv.org/abs/2307.09142
Autor:
Kiyani, Elham, Sarvestani, Hamidreza Yazdani, Ravanbakhsh, Hossein, Behbahani, Razyeh, Ashrafi, Behnam, Rahmat, Meysam, Karttunen, Mikko
Topologically interlocking architectures can generate tough ceramics with attractive thermo-mechanical properties. This concept can make the material design pathway a challenging task, since modeling the whole design space is neither effective nor fe
Externí odkaz:
http://arxiv.org/abs/2305.11632
We propose a framework and an algorithm to uncover the unknown parts of nonlinear equations directly from data. The framework is based on eXtended Physics-Informed Neural Networks (X-PINNs), domain decomposition in space-time, but we augment the orig
Externí odkaz:
http://arxiv.org/abs/2305.10706
Autor:
Jenni Karttunen, Lajos Kalmar, Andrew Grant, Jun Ying, Sarah E. Stewart, Xiaonan Wang, Fiona Karet Frankl, Tim Williams
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Current diagnostic methods for canine urothelial carcinoma (UC) are technically challenging or can lack specificity, hence there is a need for novel biomarkers of UC. To this end, we analysed the microRNA (miRNA) cargo of extracellular vesic
Externí odkaz:
https://doaj.org/article/408c8c855f354cefac0f325e0b50062d
Tissue growth kinetics and interface dynamics depend on the properties of the tissue environment and cell-cell interactions. In cellular environments, substrate heterogeneity and geometry arise from a variety factors, such as the structure of the ext
Externí odkaz:
http://arxiv.org/abs/2303.10850
Radio wave propagation simulations based on the ray-optical approximation have been widely adopted in coverage analysis for a range of situations, including the outdoor-to-indoor scenario. This work presents O2I ray-tracing simulations utilizing a co
Externí odkaz:
http://arxiv.org/abs/2210.03159