Implementation of two-step Runge-Kutta methods for ordinary differential equations

AbstractWe investigate the potential for efficient implementation of two-step Runge-Kutta methods (TSRK), a new class of methods introduced recently by Jackiewicz and Tracogna for numerical integration of ordinary differential equations. The implementation issues addressed are the local error estimation, changing stepsize using Nordsieck technique and construction of interpolants. The numerical experiments indicate that the constructed error estimates are very reliable in a fixed and variable stepsize environment

Performance Improvements for Nuclear Reaction Network Integration

Aims: The aim of this work is to compare the performance of three reaction network integration methods used in stellar nucleosynthesis calculations. These are the Gear's backward differentiation method, Wagoner's method (a 2nd-order Runge-Kutta method), and the Bader-Deuflehard semi-implicit multi-step method. Methods: To investigate the efficiency of each of the integration methods considered here, a test suite of temperature and density versus time profiles is used. This suite provides a range of situations ranging from constant temperature and density to the dramatically varying conditions present in white dwarf mergers, novae, and x-ray bursts. Some of these profiles are obtained separately from full hydrodynamic calculations. The integration efficiencies are investigated with respect to input parameters that constrain the desired accuracy and precision. Results: Gear's backward differentiation method is found to improve accuracy, performance, and stability in integrating nuclear reaction networks. For temperature-density profiles that vary strongly with time, it is found to outperform the Bader-Deuflehard method (although that method is very powerful for more smoothly varying profiles). Wagoner's method, while relatively fast for many scenarios, exhibits hard-to-predict inaccuracies for some choices of integration parameters owing to its lack of error estimations.Comment: 13 pages, 12 figures, accepted to Astronomy and Astrophysics (section 15) - corrected units in Figs. 6-1

Guidance, flight mechanics and trajectory optimization. Volume 6 - The N-body problem and special perturbation techniques

Analytical formulations and numerical integration methods for many body problem and special perturbative technique

Probabilistic Numerics and Uncertainty in Computations

We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data has led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimisers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.Comment: Author Generated Postprint. 17 pages, 4 Figures, 1 Tabl

Calibrated Adaptive Probabilistic ODE Solvers

Probabilistic solvers for ordinary differential equations assign a posterior measure to the solution of an initial value problem. The joint covariance of this distribution provides an estimate of the (global) approximation error. The contraction rate of this error estimate as a function of the solver's step size identifies it as a well-calibrated worst-case error, but its explicit numerical value for a certain step size is not automatically a good estimate of the explicit error. Addressing this issue, we introduce, discuss, and assess several probabilistically motivated ways to calibrate the uncertainty estimate. Numerical experiments demonstrate that these calibration methods interact efficiently with adaptive step-size selection, resulting in descriptive, and efficiently computable posteriors. We demonstrate the efficiency of the methodology by benchmarking against the classic, widely used Dormand-Prince 4/5 Runge-Kutta method.Comment: 17 pages, 10 figures

Spitzer 24 micron Survey of Debris Disks in the Pleiades

We performed a 24 micron 2 Deg X 1 Deg survey of the Pleiades cluster, using the MIPS instrument on Spitzer. Fifty four members ranging in spectral type from B8 to K6 show 24 micron fluxes consistent with bare photospheres. All Be stars show excesses attributed to free-free emission in their gaseous envelopes. Five early-type stars and four solar-type stars show excesses indicative of debris disks. We find a debris disk fraction of 25 % for B-A members and 10 % for F-K3 ones. These fractions appear intermediate between those for younger clusters and for the older field stars. They indicate a decay with age of the frequency of the dust-production events inside the planetary zone, with similar time scales for solar-mass stars as have been found previously for A-stars.Comment: accepted to Ap

Economic MPC with Modifier Adaptation using Transient Measurements

Producción CientíficaThis paper presents a method to estimate process dynamic gradients along the transient that combined with the idea of Modifier Adaptation (MA) improves the economic cost fuction of the examples presented. The gradient estimation method, called TMA, aims to reduce the large convergence time required to traditional MA in processes of slow dynamics. TMA is used with an economic predictive control with MA (eMPC+TMA) and was applied in two case studies: a simulation of the Williams-Otto reactor and a hybrid laboratory plant based on the Van de Vusse reactor. The results show that eMPC+TMA could reach the plant real steady-state optimum despite process-model mismatch, due to the inclusion of the effect of process dynamics in the TMA algorithm. Despite the estimation errors, the proposed methodology improved the profit of the experimental case study, with respect to the use of an eMPC with no modifiers, by about 20% for the unconstrained case, and by 130% in the constrained case.Junta de Castilla y León (CLU 2017-09 and UIC 233)FEDER - AEI (PGC2018-099312-B-C31