Parallelized Global Stochastic Optimization for Efficient Ensemble Enhancement within an Adaptive Monte Carlo Forecasting Platform

VanFossen, Andrew W.; Kumar, Mrinal

doi:10.2514/6.2021-1857

Cited by 2 publications

(7 citation statements)

References 24 publications

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“…That is, for strict error control scenarios, ( 5) is expected to trigger AMC ensemble adaptations throughout the simulation's duration. Utilizing (5) here also provides the parallel AMC platform with comparison points found in [28] and [23] for baselines before moving to higher dimensional systems defined by the Lorenz-96 and Lotka-Volterra models in the succeeding sections.…”

Section: A Entry Descent and Landingmentioning

confidence: 99%

“…(1) was replaced with Alg. ( 2) in [28], which utilizes a stochastic optimization routine known as SA to solve (15). SA is a class of gradient-free randomized algorithms that search for the global minimum of a cost function, in this case discrepancy for a given ensemble size n, by gradually reducing the magnitude of the random perturbations with respect to the current state.…”

Section: B Ensemble Enhancer: Samentioning

confidence: 99%

“…As described in [28], SA offers an alternative approach to the original architecture of the ensemble enhancer within AMC. SA has been utilized to study the traveling salesman problem, in circuit design, the design of decision trees, in data analysis, imaging and neural networks, as well as in areas of biology, physics, finance, and the military, among others [52], [53], [54], [55], [56], [57], [58], [59], [60], [61].…”

Section: A State Of the Art: Parallel Samentioning

confidence: 99%

“…Another design choice the AMC platform makes is the serial computation of the first temperature cycle for all CPUs. As stated in [28] and depicted in Fig. (6), the magnitude of the cost function perturbation inherited by the children CPUs is designed to settle as the execution timeline progresses.…”

Section: B Parallel Sa For Dependent Costsmentioning

confidence: 99%

“…When SA cools, exploration of the entire domain becomes less important as the cost function related to T i and T i−1 is nearly identical. Even though parallel SA alleviates a significant computational burden within the ensemble enhancer, data communication and resynchronization of each CPU in parallel does introduce noteworthy clock time overhead in comparison to serial computations, which should be considered [28], [78].…”

Section: B Parallel Sa For Dependent Costsmentioning

confidence: 99%

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Efficient Adaptive Monte Carlo Uncertainty Forecasting for High Dimensional Nonlinear Dynamic Systems

VanFossen

Kumar

2023

IEEE Access

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The problem of uncertainty forecasting for complex dynamical systems in the framework of particle methods can be effectively addressed through the solution methodology known as adaptive Monte Carlo (AMC). Monte Carlo (MC) methods involve discretizing the initial probability density function (pdf) followed by forward propagation of particles through system dynamics to obtain an approximate particle representation of the evolved state uncertainty. While simple to implement, MC faces questions surrounding transient statistical consistency and rate of convergence. AMC (adopted here) addresses these issues on-the-fly using defined bounds on estimation accuracy alongside ensemble enrichment routines. This paper presents several improvements in the ensemble enrichment module of AMC. The AMC platform is re-engineered to include the novel implementation of a parallel global stochastic optimization routine in conjunction with additional module enhancements that work together towards the goal of efficient forecasting. Moreover, the efficacy of algorithms utilized within every submodule of AMC are detailed and improved upon under the framework of arithmetic minimization and parallelization. Each submodule is profiled to determine computational bottlenecks, optimized or replaced with more efficient methods such as simulated annealing (SA), and parallelized ultimately leading to a clock time reduction of 200 − 400% for benchmark entry descent and landing, Lorenz-96, and Lotka-Volterra models.

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Section: A Entry Descent and Landingmentioning

confidence: 99%

Section: B Ensemble Enhancer: Samentioning

confidence: 99%