Solving partial differential equations using large-data models: a literature review
Sign Up to like & getrecommendations! Published in 2024 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-024-10784-5
Abstract: Mathematics lies at the heart of engineering science and is very important for capturing and modeling of diverse processes. These processes may be naturally-occurring or man-made. One important engineering problem in this regard is the… read more here.
Keywords: differential equations; area; using large; data models ... See more keywords
Determining radial efficiency with a large data set by solving small-size linear programs
Sign Up to like & getrecommendations! Published in 2017 at "Annals of Operations Research"
DOI: 10.1007/s10479-015-1968-4
Abstract: This paper presents a new algorithm for determining radial efficiency with a large data set by using small-size linear programs (LPs). Instead of trying to “reduce” the size of individual LPs, the proposed algorithm attempts… read more here.
Keywords: radial efficiency; large data; efficiency large; size ... See more keywords
Scaling Large Data Center Interconnects: Challenges and Solutions
Sign Up to like & getrecommendations! Published in 2017 at "Optical Fiber Technology"
DOI: 10.1016/j.yofte.2017.10.002
Abstract: Abstract Increased demands for web and cloud-based services have been driving exponential growth of datacenter bandwidth. This paper discusses, from Google’s perspective, emerging challenges and possible technical solutions for scaling intra-datacenter and intra-campus interconnection network… read more here.
Keywords: center interconnects; large data; data center; scaling large ... See more keywords
Large Data Set-Driven Machine Learning Models for Accurate Prediction of the Thermoelectric Figure of Merit.
Sign Up to like & getrecommendations! Published in 2022 at "ACS applied materials & interfaces"
DOI: 10.1021/acsami.2c15396
Abstract: The figure of merit (zT) is a key parameter to measure the performance of thermoelectric materials. At present, the prediction of zT values via machine leaning has emerged as a promising method for exploring high-performance… read more here.
Keywords: large data; machine learning; prediction; figure merit ... See more keywords
Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set—An Environmental In Vivo Case Study
Sign Up to like & getrecommendations! Published in 2024 at "ACS Applied Materials & Interfaces"
DOI: 10.1021/acsami.4c07153
Abstract: The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long.… read more here.
Keywords: doped cuo; cuo library; machine learning; soil ... See more keywords
A robust data-driven approach identifies four personality types across four large data sets
Sign Up to like & getrecommendations! Published in 2018 at "Nature Human Behaviour"
DOI: 10.1038/s41562-018-0419-z
Abstract: Understanding human personality has been a focus for philosophers and scientists for millennia1. It is now widely accepted that there are about five major personality domains that describe the personality profile of an individual2,3. In… read more here.
Keywords: data sets; four large; large data; personality ... See more keywords
On the Performance of Mean-Based Sort for Large Data Sets
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3063205
Abstract: Computer and communication systems and networks deal with many cases that require rearrangement of data either in descending or ascending order. This operation is called sorting, and the purpose of an efficient sorting algorithm is… read more here.
Keywords: large data; time; data sets; mean based ... See more keywords
Inter-Datacenter Scheduling of Large Data Flows
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Cloud Computing"
DOI: 10.1109/tcc.2015.2487964
Abstract: Inter-datacenter transfers of non-interactive but timely large flows over a private (managed) network is an important problem faced by many cloud service providers. The considered flows are non-interactive because they do not explicitly target the… read more here.
Keywords: large data; inter datacenter; datacenter; datacenter scheduling ... See more keywords
A Kriging-Based Optimization Approach for Large Data Sets Exploiting Points Aggregation Techniques
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Magnetics"
DOI: 10.1109/tmag.2017.2665703
Abstract: A kriging-based optimization approach is proposed for problems with large data sets and high dimensionality. Memory usage is maintained via model centering aided by minimizing the impact of information loss on accuracy of new point… read more here.
Keywords: data sets; optimization approach; based optimization; large data ... See more keywords
Optimizing Grid Layouts for Level‐of‐Detail Exploration of Large Data Collections
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DOI: 10.1111/cgf.14537
Abstract: This paper introduces an optimization approach for generating grid layouts from large data collections such that they are amenable to level‐of‐detail presentation and exploration. Classic (flat) grid layouts visually do not scale to large collections,… read more here.
Keywords: large data; grid layouts; exploration; level detail ... See more keywords
Existence of large-data global-in-time finite-energy weak solutions to a compressible FENE-P model
Sign Up to like & getrecommendations! Published in 2018 at "Mathematical Models and Methods in Applied Sciences"
DOI: 10.1142/s0218202518500471
Abstract: A compressible FENE-P-type model with stress diffusion is derived from an approximate macroscopic closure of a compressible Navier–Stokes–Fokker–Planck system arising in the kinetic theory of dilut... read more here.
Keywords: compressible fene; global time; data global; large data ... See more keywords