用量子Monte Carlo方法计算氢分子H_2基态(Χ~1∑_g~+)势能曲线

本文用量子MontoCarlo方法中优化试探波函数Ψ_T计算氢分子H_2基态(X~1∑_g~+)势能曲线.文中采用相当简单的波函数形式,并用固定样点优化技术优化试探波函数的参数.确定优化试探波函数后,分别用变分Monte Carlo及固定节面M0nte Carlo计算势能曲线各点能值.二种方法先后得95%和100%的相关能.因此,在量子M0nte Carlo方法中,用本文作者提出的试探波函数计算分子势能面,将会获得很好的结果.从而对分子散射和动力学的研究有重要意义.

Optimization of tried wave-function in quantum monte-carlo method

A method for optimizing tried wave functions in quantum Monte Carlo method has been found and used to calculate the energies of molecules, such as H-2, Li-2, H-3+, H-3 and H-4. Good results were obtained.

Monte Carlo Valuation of natural gas investments

This paper deals with the valuation of energy assets related to natural gas. In particular, we evaluate a baseload Natural Gas Combined Cycle (NGCC) power plant and an ancillary instalation, namely a Liquefied Natural Gas (LNG) facility, in a realistic setting; specifically, these investments enjoy a long useful life but require some non-negligible time to build. Then we focus on the valuation of several investment options again in a realistic setting. These include the option to invest in the power plant when there is uncertainty concerning the initial outlay, or the option's time to maturity, or the cost of CO2 emission permits, or when there is a chance to double the plant size in the future. Our model comprises three sources of risk. We consider uncertain gas prices with regard to both the current level and the long-run equilibrium level; the current electricity price is also uncertain. They all are assumed to show mean reversion. The two-factor model for natural gas price is calibrated using data from NYMEX NG futures contracts. Also, we calibrate the one-factor model for electricity price using data from the Spanish wholesale electricity market, respectively. Then we use the estimated parameter values alongside actual physical parameters from a case study to value natural gas plants. Finally, the calibrated parameters are also used in a Monte Carlo simulation framework to evaluate several American-type options to invest in these energy assets. We accomplish this by following the least squares MC approach.