2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
Tensor Methods: A Unified and Interpretable Approach for Material Design
Shaan Pakala, Aldair E. Gongora, Brian Giera +1
When designing new materials, it is often necessary to tailor the material design to have some desired properties. As the set of material design parameters grows, the search space…
Surrogate Modeling for the Design of Optimal Lattice Structures using Tensor Completion
Shaan Pakala, Aldair E. Gongora, Brian Giera +1
When designing new materials, it is often necessary to design a material with specific desired properties. Unfortunately, as new design variables are added, the search space grows…
Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization
Paimon Goulart, Shaan Pakala, Evangelos Papalexakis
Producing large complex simulation datasets can often be a time and resource consuming task. Especially when these experiments are very expensive, it is becoming more reasonable to…
Tensor Completion for Surrogate Modeling of Material Property Prediction
Shaan Pakala, Dawon Ahn, Evangelos Papalexakis
When designing materials to optimize certain properties, there are often many possible configurations of designs that need to be explored. For example, the materials' composition o…
Automating Data Science Pipelines with Tensor Completion
Shaan Pakala, Bryce Graw, Dawon Ahn +5
Hyperparameter optimization is an essential component in many data science pipelines and typically entails exhaustive time and resource-consuming computations in order to explore t…