J
John Daniel Siirola
Researcher at Sandia National Laboratories
Publications - 44
Citations - 633
John Daniel Siirola is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Optimization problem & Stochastic programming. The author has an hindex of 11, co-authored 41 publications receiving 417 citations. Previous affiliations of John Daniel Siirola include Carnegie Mellon University.
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Journal ArticleDOI
pyomo.dae : a modeling and automatic discretization framework for optimization with differential and algebraic equations
Bethany L. Nicholson,John Daniel Siirola,Jean-Paul Watson,Victor M. Zavala,Lorenz T. Biegler +4 more
TL;DR: Pyomo.dae is an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks.
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Operation of high-speed silicon photonic micro-disk modulators at cryogenic temperatures
Michael Gehl,Christopher M. Long,Doug C. Trotter,Andrew Starbuck,Andrew Pomerene,Jeremy B. Wright,Seth D. Melgaard,John Daniel Siirola,Anthony L. Lentine,Christopher T. DeRose +9 more
TL;DR: In this paper, the authors demonstrate the operation of a high-speed, CMOS compatible silicon micro-disk modulator transmitting data at rates up to 10 Gb/s and at temperatures down to 4.8 K.
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A stochastic programming approach for gas detector placement using CFD-based dispersion simulations
Sean Legg,A. J. Benavides-Serrano,John Daniel Siirola,Jean-Paul Watson,S. G. Davis,A. Bratteteig,Carl D. Laird +6 more
TL;DR: Results show that the additional coverage constraint significantly improves performance on alternate subsamples, making a strong case for the use of rigorous dispersion simulation coupled with stochastic programming to improve the effectiveness of these safety systems.
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Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems
Akshaya Upadhyay,Can Li,Antonio J. Conejo,Peng Liu,Benjamin P. Omell,John Daniel Siirola,Ignacio E. Grossmann +6 more
TL;DR: Using a case study from Electric Reliability Council of Texas (ERCOT), it is shown that the proposed tailored Benders decomposition outperforms the nested Bender decomposition in solving GEP and TEP simultaneously.
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Toward agent-based process systems engineering: proposed framework and application to non-convex optimization
TL;DR: A modular framework for implementing agent-based systems for engineering design is proposed by identifying multiple identical global optima for a non-convex optimization problem with numerous local minima and shows that inter- and intra-agent collaboration have a significant impact on system performance.