John Tencer
Sandia National Laboratories
AlgorithmPhysicsStatistical physicsK-distributionMathematical analysisFinite element methodPercolationRadiationHeat fluxPiecewiseSet (abstract data type)Boundary value problemMaterials scienceModel order reductionDiscrete Ordinates MethodMulti-sourceApplied mathematicsComputational physicsMathematicsComputer scienceHeat transferMechanicsRadiative fluxDiscretizationRadiative transferConvolutional neural networkThermal radiationReduction (complexity)
30Publications
6H-index
82Citations
Publications 29
Newest
#1Francesco RizziH-Index: 9
#2Eric J. ParishH-Index: 9
Last. John TencerH-Index: 6
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This work aims to advance computational methods for projection-based reduced order models (ROMs) of linear time-invariant (LTI) dynamical systems. For such systems, current practice relies on ROM formulations expressing the state as a rank-1 tensor (i.e., a vector), leading to computational kernels that are memory bandwidth bound and, therefore, ill-suited for scalable performance on modern many-core and hybrid computing nodes. This weakness can be particularly limiting when tackling many-query ...
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#1Kevin Potter (Georgia Institute of Technology)H-Index: 2
#2Steven Richard Sleder (SNL: Sandia National Laboratories)
Last. John Tencer (SNL: Sandia National Laboratories)H-Index: 6
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We present a novel graph convolutional layer that is fast, conceptually simple, and provides high accuracy with reduced overfitting. Based on pseudo-differential operators, our layer operates on graphs with relative position information available for each pair of connected nodes. We evaluate our method on a variety of supervised learning tasks, including superpixel image classification using the MNIST, CIFAR10, and CIFAR100 superpixel datasets, node correspondence using the FAUST dataset, and sh...
#1Francesco RizziH-Index: 9
#2Eric J. ParishH-Index: 9
Last. John TencerH-Index: 6
view all 4 authors...
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#1John Tencer (SNL: Sandia National Laboratories)H-Index: 6
#2Kelsey Meeks Forsberg (SNL: Sandia National Laboratories)
In this work, we revisit the classic problem of site percolation on a regular square lattice. In particular, we investigate the effect of quantization bias errors on percolation threshold predictions for large probability gradients and propose a mitigation strategy. We demonstrate through extensive computational experiments that the assumption of a linear relationship between probability gradient and percolation threshold used in previous investigations is invalid. Moreover, we demonstrate that,...
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#1Marco Arienti (SNL: Sandia National Laboratories)H-Index: 11
#2Patrick J. Blonigan (SNL: Sandia National Laboratories)H-Index: 11
Last. Micah Howard (SNL: Sandia National Laboratories)H-Index: 4
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#1John TencerH-Index: 6
#2Kevin PotterH-Index: 2
Last. Kevin PotterH-Index: 2
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We propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov n-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with comple...
2 CitationsSource
#1John TencerH-Index: 6
#2Kevin PotterH-Index: 2
Last. Kevin PotterH-Index: 2
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We propose a nonlinear manifold learning technique based on deep autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for systems demonstrating a slow-decaying Kolmogorov n-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with complex geometry. Our custom graph convolution ope...
3 Citations
#1John Tencer (SNL: Sandia National Laboratories)H-Index: 6
#2Kevin Carlberg (SNL: Sandia National Laboratories)H-Index: 17
Last. Roy E. Hogan (SNL: Sandia National Laboratories)H-Index: 9
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4 CitationsSource
#1Flint Pierce (SNL: Sandia National Laboratories)H-Index: 3
#2John Tencer (SNL: Sandia National Laboratories)H-Index: 6
Last. Clifton Russell Drumm (SNL: Sandia National Laboratories)H-Index: 4
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#1Flint Pierce (SNL: Sandia National Laboratories)H-Index: 3
#2John Tencer (SNL: Sandia National Laboratories)H-Index: 6
Last. Clifton Russell Drumm (SNL: Sandia National Laboratories)H-Index: 4
view all 0 authors...
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