History Matching of Gas Simulation Models Using Optimal Control Theory

1975 ◽  
Author(s):  
E.L. Dougherty ◽  
D. Khairkhah
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Optimal Control Theory ◽  
History Matching ◽  
Simulation Models

scholarly journals Applying optimal control theory to complex epidemiological models to inform real-world disease management

2018 ◽  
Author(s):  
E.H. Bussell ◽  
C.E. Dangerfield ◽  
C.A. Gilligan ◽  
N.J. Cunniffe
Keyword(s):  
Optimal Control ◽  
Disease Management ◽  
Control Theory ◽  
Simulation Model ◽  
Optimal Control Theory ◽  
Management Strategies ◽  
Simulation Models ◽  
Open Loop ◽  
Small Subset ◽  
Control Disease

SummaryMathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially-explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach – optimal control theory – allows the very best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of plants, animals and humans.

scholarly journals Ongoing surveillance protects tanoak whilst conserving biodiversity: applying optimal control theory to a spatial simulation model of sudden oak death

2019 ◽  
Author(s):  
E.H. Bussell ◽  
N.J. Cunniffe
Keyword(s):  
Optimal Control ◽  
Disease Management ◽  
Control Theory ◽  
Predictive Control ◽  
Optimal Control Theory ◽  
Control Strategies ◽  
Management Strategies ◽  
Simulation Models ◽  
Sudden Oak Death ◽  
Ongoing Surveillance

AbstractThe sudden oak death epidemic in California is spreading uncontrollably. Large-scale eradication has been impossible for some time. However, small-scale disease management could still slow disease spread. Although empirical evidence suggests localised control could potentially be successful, mathematical models have said little about such management. By approximating a detailed, spatially-explicit simulation model of sudden oak death with a simpler, mathematically-tractable model, we demonstrate how optimal control theory can be used to unambiguously characterise effective time-dependent disease management strategies. We focus on protection of tanoak, a tree species which is culturally and ecologically important, but also highly susceptible to sudden oak death. We identify management strategies to protect tanoak in a newly-invaded forest stand, whilst also conserving biodiversity. We find that thinning of bay laurel is essential early in the epidemic. We apply model predictive control, a feedback strategy in which both the approximating model and the control are repeatedly updated as the epidemic progresses. Adapting optimal control strategies in this way is vital for effective disease management. This feedback strategy is robust to parameter uncertainty, limiting loss of tanoak in the worst-case scenarios. However, the methodology requires ongoing surveillance to re-optimise the approximating model. This introduces an optimal level of surveillance to balance the high costs of intensive surveys against improved management resulting from better estimates of disease progress. Our study shows how detailed simulation models can be coupled with optimal control theory and model predictive control to find effective control strategies for sudden oak death. We demonstrate that control strategies for sudden oak death must depend on local management goals, and that success relies on adaptive strategies that are updated via ongoing disease surveillance. The broad framework allowing the use of optimal control theory on complex simulation models is applicable to a wide range of systems.

Dispersion of nanoparticles with optimal control theory based on destabilization

2014 ◽  
Vol 2 ◽  
pp. 86-86
Author(s):  
Miki U. Kobayashi ◽  
Nobuaki Aoki ◽  
Noriyoshi Manabe ◽  
Tadafumi Adschiri
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Optimal Control Theory ◽  
Dispersion Of Nanoparticles

scholarly journals Recoil control of deepwater drilling riser system based on optimal control theory

2020 ◽  
pp. 108473
Author(s):  
Xiuquan Liu ◽  
Zhaowei Liu ◽  
Xianglei Wang ◽  
Nan Zhang ◽  
Na Qiu ◽  
...  
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Optimal Control Theory ◽  
Deepwater Drilling

scholarly journals Optimal Control for a COVID-19 Model Accounting for Symptomatic and Asymptomatic

2020 ◽  
Vol 8 (1) ◽  
pp. 168-179
Author(s):  
Jead M. Macalisang ◽  
Mark L. Caay ◽  
Jayrold P. Arcede ◽  
Randy L. Caga-anan
Keyword(s):  
Optimal Control ◽  
Control Theory ◽  
Medical Care ◽  
Optimal Control Theory ◽  
Comparable Result ◽  
Type Model ◽  
Hospital Beds ◽  
Transmission Reduction ◽  
Bed Capacity ◽  
The Government

AbstractBuilding on an SEIR-type model of COVID-19 where the infecteds are further divided into symptomatic and asymptomatic, a system incorporating the various possible interventions is formulated. Interventions, also referred to as controls, include transmission reduction (e.g., lockdown, social distancing, barrier gestures); testing/isolation on the exposed, symptomatic and asymptomatic compartments; and medical controls such as enhancing patients’ medical care and increasing bed capacity. By considering the government’s capacity, the best strategies for implementing the controls were obtained using optimal control theory. Results show that, if all the controls are to be used, the more able the government is, the more it should implement transmission reduction, testing, and enhancing patients’ medical care without increasing hospital beds. However, if the government finds it very difficult to implement the controls for economic reasons, the best approach is to increase the hospital beds. Moreover, among the testing/isolation controls, testing/isolation in the exposed compartment is the least needed when there is significant transmission reduction control. Surprisingly, when there is no transmission reduction control, testing/isolation in the exposed should be optimal. Testing/isolation in the exposed could seemingly replace the transmission reduction control to yield a comparable result to that when the transmission reduction control is being implemented.

Low-power homonuclear dipolar recoupling in solid-state NMR developed using optimal control theory

2005 ◽  
Vol 414 (1-3) ◽  
pp. 204-209 ◽  
Author(s):  
Cindie Kehlet ◽  
Thomas Vosegaard ◽  
Navin Khaneja ◽  
Steffen J. Glaser ◽  
Niels Chr. Nielsen
Keyword(s):  
Optimal Control ◽  
Solid State ◽  
Control Theory ◽  
Low Power ◽  
Optimal Control Theory ◽  
Solid State Nmr ◽  
Dipolar Recoupling
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