Each had different programs that tried to unify computational efforts, materials science information, and applied mechanics algorithms with different levels of success. Multiple scientific articles were written, and the multiscale activities took different lives of their own. At SNL, the multiscale modeling effort was an engineering top-down approach starting from continuum mechanics perspective, which was already rich with a computational paradigm. SNL tried to merge the materials science community into the continuum mechanics community to address the lower-length scale issues that could help solve engineering problems in practice.
- We thank N.Kallikounis (ETH Zurich) for helpful discussions on the LB method, P.
- It can be used to describe any situation where a physical problem is solved by capturing a system’s behavior and important features at multiple scales, particularly multiple spatial and/or temporal scales.
- This allows an analyst to identify which details and relationships in the fine scale representation of a system have large scale implications, and which details disappear at coarser scales.
- The finger seal is an advanced flexible seal technology which is applied to aero engines1.
- To quantify the performance of data integration we used metrics to assess biological conservation and batch correction proposed in ref. 6 (Methods).
- A large number of such methods have been developed, taking a range of approaches to bridging across multiple length and time scales.
Chemical Engineering and Processing
New theory-driven approaches could provide a rigorous foundation to estimate the range of validity, quantify the uncertainty, and characterize the level of confidence of machine learning based approaches. Accelerating model- and data-driven discovery by integrating theory-driven machine learning and multiscale modeling. Theory-driven machine learning can yield data-efficient workflows for predictive modeling by synthesizing prior knowledge and multimodality data at different scales. Probabilistic formulations can also enable the quantification of predictive uncertainty and guide the judicious acquisition of new data in a dynamic model-refinement setting. Hierarchy theory is an older approach to multiscale analysis that arose within GST.
Implementation of multispecies ecological networks at the regional scale: analysis and multi-temporal assessment
Our study showed that the regional water quality was relatively worse in the district due to its higher population density and higher urbanization ratio. When flowing through highly urbanized areas, the water quality of the rivers became worse due to many industrial sectors such as mechanical and chemical factories. We have presented scPoli, a generative model for data integration, label transfer and reference mapping. ScPoli learns representations of the input data at different scales by learning cell and sample embeddings. This enables multi-scale analyses whereby the user can explore sample information in a dedicated latent space, while still having access to an integrated single-cell object. By freezing the weights of the model and learning new embeddings, scPoli is able to quickly map newly generated data onto a previously built reference.
A conceptual model for analyzing the stability condition and regime transition in bubble columns
The aim is to suppress noise as much as possible while preserving image features. Scale invariance, fractal statistics, the fractal dimension and measures of selfsimilarity also provide insight into the relationship between scales within a system. For example, these techniques may reveal limits to the utility of averages, the dependence of a measure on the scale of measurement, and the mutual information between scales of a system.
Multiscale Methods for Fracture: A Review\(^\bigstar \)
undefined stands for the usual closing-opening using a disk of radius r as uniform SE (resp. using the adaptive SEs with the homogeneity tolerance m, and the criterion mapping h within the CLIP framework).
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As materials continue to advance, it is becoming increasingly important to not only examine them at ever-higher resolutions but to obtain these observations within the relevant macroscopic context. This necessitates correlating different imaging modes to the same coordinates for truly contextual insight. Measurements must also be obtained quickly enough for practical application in real-world process control and failure analysis environments. Thermo Fisher Scientific offers a complete workflow for the observation of materials, combining correlated imaging at various scales with additional information such as chemical composition.
Optimize multiscale feature hybrid-net deep learning approach used for automatic pancreas image segmentation
Blood samples were collected in heparinized vacutainer tubes in the early morning after an overnight fast, with samples exhibiting hemolysis or lipemic interference excluded. The blood samples were allowed to settle, then centrifuged at 3,000 rpm for 10 min (room temperature). The fecal multi-scale analysis and serum samples were immediately placed in liquid nitrogen after collection for 15 min and subsequently stored at -80 °C.