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Publication	Type	Year
Python Data-Driven Model Integration for the Xyce Circuit Simulator 2022 Sandia Machine Learning and Deep Learning Workshop Paul Allen Kuberry, Biliana Stefanova Paskaleva, Ting Mei, Eric R. Keiter, Pavel B. Bochev, Thomas E. Buchheit, Matthew R. Glickman	Conference Presentation – 2022 Conference Presentation	2022
Python Data-Driven Model Integration for the Xyce Circuit Simulator 2022 Tri-lab Advanced Simulation & Computing Sustainable Scientific Software Conference Paul Allen Kuberry, Biliana Stefanova Paskaleva, Ting Mei, Eric R. Keiter, Pavel B. Bochev, Thomas E. Buchheit, Matthew R. Glickman	Conference Presentation – 2022 Conference Presentation	2022
A Compact Delayed Photocurrent Model Based on a Reduced Order Data-Driven Exponential Time Integrator Earth and Space 2022 Kenneth Chadwick Sockwell, Pavel B. Bochev, Biliana Stefanova Paskaleva	Conference Presentation – 2022 Conference Presentation	2022
Data-Driven Approximation of the Koopman Operator to support the Design of Electrical Systems, Data-Driven Model Integration for the Xyce Circuit Simulator, A nonlinear numerical transient compact photocurrent model and two more 2022 SIAM Conference on Mathematics of Data Science Joshua McKinley Hanson, Andy Huang, Edgar Galvan, Kenneth Chadwick Sockwell, Biliana Stefanova Paskaleva, Charles E. Hembree, Pavel B. Bochev	Abstract – 2022 Abstract	2022
Development of Compact Circuit Models using Generalized Hammerstein-Wiener Models 2022 SIAM Conference on Mathematics of Data Science Biliana Stefanova Paskaleva, Kenneth Chadwick Sockwell, Pavel B. Bochev	Abstract – 2022 Abstract	2022
Semiconductor Device Models Constructed Using Simple Elements and Auxiliary Dynamics Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Charles E. Hembree, Joshua McKinley Hanson, Biliana Stefanova Paskaleva, Pavel B. Bochev, Eric R. Keiter, Alan Mar, Ting Mei	Conference Presentation – 2021 Conference Presentation	2021
Xyce Python Model Interpreter (Xyce-PyMi) for enabling ML advancements in production circuit simulation software Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Paul Allen Kuberry, Ting Mei, Eric R. Keiter, Biliana Stefanova Paskaleva, Pavel B. Bochev, Joshua McKinley Hanson	Conference Presentation – 2021 Conference Presentation	2021
Data-Driven Compact Modeling of Bipolar Junction Transistors with Recurrent Neural Networks Mmld-cest-2021 Biliana Stefanova Paskaleva, Viraj Shitole, Elyse Rosenbaum, Joshua McKinley Hanson, Alan Yang, Paul Allen Kuberry	Conference Presentation – 2021 Conference Presentation	2021
Hybrid analytic-numerical compact models for radiation-induced photocurrents. 18th Earth and Space Conference Pavel B. Bochev, Joshua McKinley Hanson, Biliana Stefanova Paskaleva, Charles E. Hembree, Eric R. Keiter, Kenneth Chadwick Sockwell	Abstract – 2021 Abstract	2021
A Numerical Compact Photocurrent Model and its Implementation via Xyce-PyMi Machine Learning and Deep Learning Conference (MLDL 2021) Joshua McKinley Hanson, Biliana Stefanova Paskaleva, Pavel B. Bochev, Eric R. Keiter, Charles E. Hembree, Paul Allen Kuberry https://www.osti.gov/search/identifier:1876997	Conference Presentation – 2021 Conference Presentation	2021
Data-driven compact modeling of bipolar junction transistors with recurrent neural networks Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering and Technology (MMLDT-CSET) Joshua McKinley Hanson, Biliana Stefanova Paskaleva, Paul Allen Kuberry, Elyse Rosenbaum, Alan Yang, Viraj Shitole	Abstract – 2021 Abstract	2021
Enabling Python physics-aware machine learned device models in Xyce Mmldt-cset 2021 Paul Allen Kuberry, Eric R. Keiter, Ting Mei, Pavel B. Bochev, Biliana Stefanova Paskaleva	Abstract – 2021 Abstract	2021
A hybrid analytic-numerical compact model for radiation induced photocurrent effects Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Pavel B. Bochev, Joshua McKinley Hanson, Biliana Stefanova Paskaleva, Eric R. Keiter, Charles E. Hembree	Abstract – 2021 Abstract	2021
Learning Compact Physics-Aware Photocurrent Models Using Dynamic Mode Decomposition SEERI End of Summer Presentations Joshua McKinley Hanson, Pavel B. Bochev, Biliana Stefanova Paskaleva https://www.osti.gov/search/identifier:1813918	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Data-Driven Compact Models for Circuit Design and Analysis MSMLMathematical and Scientific Machine Learning Workshop Karthik Venkatraman Aadithya, Paul Allen Kuberry, Biliana Stefanova Paskaleva, Pavel B. Bochev, Kenneth Michael Leeson, Alan Mar, Ting Mei, Eric R. Keiter https://www.osti.gov/search/identifier:1810271	Conference Paper – 2020 Conference Paper	2020
Using ML to Develop Compact Models CMC SIG Weekly Meeting Karthik Venkatraman Aadithya, Paul Allen Kuberry, Eric R. Keiter, Pavel B. Bochev, Alan Mar, Ting Mei, Biliana Stefanova Paskaleva, Kenneth Michael Leeson https://www.osti.gov/search/identifier:1779603	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Data-driven development of exponential integrators with application to photocurrent effects modeling Siam Mds 2020 Pavel B. Bochev, Biliana Stefanova Paskaleva, Paul Allen Kuberry	Abstract – 2020 Abstract	2020
Development, Demonstration and Validation of Data-driven Compact Diode Models for circuit simulation and analysis Karthik Venkatraman Aadithya, Paul Allen Kuberry, Biliana Stefanova Paskaleva, Pavel B. Bochev, Kenneth Michael Leeson, Alan Mar, Ting Mei, Eric R. Keiter https://www.osti.gov/search/identifier:1592881	Report – 2019 Report	2019
Data-driven Circuit Design From Electrical Measurement Data To Compact Device Models Machine Learning and Deep Learning Conference 2019 Karthik Venkatraman Aadithya, Paul Allen Kuberry, Eric R. Keiter, Pavel B. Bochev, Alan Mar, Ting Mei, Biliana Stefanova Paskaleva, Kenneth Michael Leeson https://www.osti.gov/search/identifier:1641510	Conference Paper – 2019 Conference Paper	2019
Using Surrogates to Calculate Sensitivities and Improve Optimization-Based Calibration Routines International Conference on Continuous Optimization (ICCOPT) John Daniel Siirola, Biliana Stefanova Paskaleva https://www.osti.gov/search/identifier:1107035	Conference Paper – 2013 Conference Paper	2013
Document Title	Type	Year

Publications

Python Data-Driven Model Integration for the Xyce Circuit Simulator

Python Data-Driven Model Integration for the Xyce Circuit Simulator

A Compact Delayed Photocurrent Model Based on a Reduced Order Data-Driven Exponential Time Integrator

Data-Driven Approximation of the Koopman Operator to support the Design of Electrical Systems, Data-Driven Model Integration for the Xyce Circuit Simulator, A nonlinear numerical transient compact photocurrent model and two more

Development of Compact Circuit Models using Generalized Hammerstein-Wiener Models

Semiconductor Device Models Constructed Using Simple Elements and Auxiliary Dynamics

Xyce Python Model Interpreter (Xyce-PyMi) for enabling ML advancements in production circuit simulation software

Data-Driven Compact Modeling of Bipolar Junction Transistors with Recurrent Neural Networks

Hybrid analytic-numerical compact models for radiation-induced photocurrents.

A Numerical Compact Photocurrent Model and its Implementation via Xyce-PyMi

Data-driven compact modeling of bipolar junction transistors with recurrent neural networks

Enabling Python physics-aware machine learned device models in Xyce

A hybrid analytic-numerical compact model for radiation induced photocurrent effects

Learning Compact Physics-Aware Photocurrent Models Using Dynamic Mode Decomposition

Data-Driven Compact Models for Circuit Design and Analysis

Using ML to Develop Compact Models

Data-driven development of exponential integrators with application to photocurrent effects modeling

Development, Demonstration and Validation of Data-driven Compact Diode Models for circuit simulation and analysis

Data-driven Circuit Design From Electrical Measurement Data To Compact Device Models

Using Surrogates to Calculate Sensitivities and Improve Optimization-Based Calibration Routines