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

#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 ...

#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...

Enabling efficient uncertainty quantification for seismic modeling via projection-based model reduction

#1Francesco RizziH-Index: 9

#2Eric J. ParishH-Index: 9

Last. John TencerH-Index: 6

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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,...

#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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A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial Discretizations

#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...

Enabling Nonlinear Manifold Projection Reduced-Order Models by Extending Convolutional Neural Networks to Unstructured Data.

#1John TencerH-Index: 6

#2Kevin PotterH-Index: 2

Last. Kevin PotterH-Index: 2

view all 2 authors...

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...

Accelerated Solution of Discrete Ordinates Approximation to the Boltzmann Transport Equation for a Gray Absorbing–Emitting Medium Via Model Reduction

#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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#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...

Close Researchers

John R. Howell

H-index : 29

Roy E. Hogan

H-index : 9

Kevin Potter

H-index : 2

Francesco Rizzi

H-index : 9

Marvin E. Larsen

H-index : 5

Kevin Carlberg

H-index : 17

Patrick J. Blonigan

H-index : 11

GiGi Gonzales

H-index : 1

Pavel Spurný

H-index : 27

Benjamin Conley

H-index : 1