Monte carlo optimization of multi-layer semiconductor material

Variance Analysis for Monte Carlo Integrationupplementary material

In this document, we provide supplementary details on various topics discussed from the main paper. In Sec. 2, we give a short background of the Representation theory framework that we used in our derivation for the spherical domain Monte Carlo (MC) variance formulation. In Sec. 3, we provide additional theoretical background of projective 2-space that allows spectral analysis of hemispherical ...

Neural Adaptive Sequential Monte Carlo Supplementary Material

This section reviews the basic SMC algorithm, beginning by recapitulating the setup described in the main text. Consider a probabilistic model comprising (possibly multi-dimensional) hidden and observed states z1:T and x1:T respectively, whose joint distribution factorizes as p(z1:T ,x1:T ) = p(z1)p(x1|z1) ∏T t=2 p(zt|z1:t−1)p(xt|z1:t,x1:t−1). This general form subsumes common statespace models...

Multi-agent Monte Carlo Go

In this paper we propose a Multi-Agent version of UCT Monte Carlo Go. We use the emergent behavior of a great number of simple agents to increase the quality of the Monte Carlo simulations, increasing the strength of the artificial player as a whole. Instead of one agent playing against itself, different agents play in the simulation phase of the algorithm, leading to a better exploration of th...

Biased Monte Carlo optimization of protein sequences

Particle Monte Carlo simulation of quantum phenomena in semiconductor nanostructures

Quantum effects in semiconductor devices are usually described in terms of wave functions obtained from the solution of the Schrödinger equation. However, it is difficult to simulate practical devices where clear semiclassical and quantum features coexist, as is the case for nanoscale devices at normal temperatures. We present here a particle description of quantum phenomena derived starting fr...