Publications

Paskaleva, Biliana S.; Shitole, Viraj S.; Rosenbaum, Elyse R.; Hanson, Joshua M.; Yang, Alan Y.; Kuberry, Paul A.

Abstract not provided.

Hembree, Charles E.; Hanson, Joshua M.; Paskaleva, Biliana S.; Bochev, Pavel B.; Keiter, Eric R.; Mar, Alan M.; Mei, Ting M.

Abstract not provided.

Kuberry, Paul A.; Mei, Ting M.; Keiter, Eric R.; Paskaleva, Biliana S.; Bochev, Pavel B.; Hanson, Joshua M.

Abstract not provided.

Hanson, Joshua M.; Paskaleva, Biliana S.; Bochev, Pavel B.; Keiter, Eric R.; Hembree, Charles E.; Kuberry, Paul A.

Abstract not provided.

Hanson, Joshua M.; Bochev, Pavel B.; Paskaleva, Biliana S.

Abstract not provided.

Aadithya, Karthik V.; Kuberry, Paul A.; Paskaleva, Biliana S.; Bochev, Pavel B.; Leeson, Kenneth M.; Mar, Alan M.; Mei, Ting M.; Keiter, Eric R.

Abstract not provided.

Aadithya, Karthik V.; Kuberry, Paul A.; Keiter, Eric R.; Bochev, Pavel B.; Mar, Alan M.; Mei, Ting M.; Paskaleva, Biliana S.; Leeson, Kenneth M.

Abstract not provided.

Aadithya, Karthik V.; Kuberry, Paul A.; Paskaleva, Biliana S.; Bochev, Pavel B.; Leeson, Kenneth M.; Mar, Alan M.; Mei, Ting M.; Keiter, Eric R.

Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (e.g., radiation effects) into an existing model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2) Generalized Moving Least-Squares, and (3) feedforward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these "data-driven" compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.

Aadithya, Karthik V.; Kuberry, Paul A.; Keiter, Eric R.; Bochev, Pavel B.; Mar, Alan M.; Mei, Ting M.; Paskaleva, Biliana S.; Leeson, Kenneth M.

Abstract not provided.

Siirola, John D.; Paskaleva, Biliana S.

Abstract not provided.

Bochev, Pavel B.; Paskaleva, Biliana S.

Arctic sea ice is an important component of the global climate system and, due to feedback effects, the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice state to internal model parameters. A new sea ice model that holds some promise for improving sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of this MPM sea ice code and compare it with the Los Alamos National Laboratory CICE code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness,and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.

Bochev, Pavel B.; Paskaleva, Biliana S.

Arctic sea ice is an important component of the global climate system and due to feedback effects the Arctic ice cover is changing rapidly. Predictive mathematical models are of paramount importance for accurate estimates of the future ice trajectory. However, the sea ice components of Global Climate Models (GCMs) vary significantly in their prediction of the future state of Arctic sea ice and have generally underestimated the rate of decline in minimum sea ice extent seen over the past thirty years. One of the contributing factors to this variability is the sensitivity of the sea ice to model physical parameters. A new sea ice model that has the potential to improve sea ice predictions incorporates an anisotropic elastic-decohesive rheology and dynamics solved using the material-point method (MPM), which combines Lagrangian particles for advection with a background grid for gradient computations. We evaluate the variability of the Los Alamos National Laboratory CICE code and the MPM sea ice code for a single year simulation of the Arctic basin using consistent ocean and atmospheric forcing. Sensitivities of ice volume, ice area, ice extent, root mean square (RMS) ice speed, central Arctic ice thickness, and central Arctic ice speed with respect to ten different dynamic and thermodynamic parameters are evaluated both individually and in combination using the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA). We find similar responses for the two codes and some interesting seasonal variability in the strength of the parameters on the solution.