PrePrint: REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling
Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations known as reduced order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods for ROM generation, automatically quantifies the ROM accuracy, and supports an iterative approach to improve ROM accuracy. REVEAL is designed to be extensible for any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can ‘mix and match’ mathematical techniques best suited to their model characteristics. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.
PrePrint: Evaluation of a special class of two-body integrals through extension of the integral routine in Maple
In this article, we investigate an integral result presented by Herrick and Stillinger in 1975 [Phys. Rev. A 11, 42 (1975)] numerically and analytically. Based on their result we implement a custom integration routine. We demonstrate that custom made integration routines can be several times faster than the native routines. This may be beneficial in cases where many integrals need to be carried out. We compared the results of custom integration routine against those of the native integration routine. For cases, where the native routine could not perform the integration, we compared the custom integration routine against a Monte Carlo estimate. In all cases, we found excellent agreement. As an application of the implemented integration routine, we compute the $N$-body matrix elements of a Hamiltonian with two-body potentials.
PrePrint: Interactive Machine Learning in Data Exploitation
The goal of interactive machine learning is to help scientists and engineers exploit more of their specialized data in less time. Interactive machine learning focuses on methods that empower domain experts to control and direct machine learning tools from within the deployed environment, whereas traditional machine learning does this in the development environment. This difference allows interactive machine learning systems to be more responsive, more accurate and cheaper to develop and maintain. This article provides a basic introduction to the main components and tries to untangle the many ideas that must be combined to produce practical interactive learning systems. It also describes recent developments in machine learning that have significantly advanced the theoretical and practical foundations for the next generation of interactive tools.
PrePrint: Simulation and visualization of few-body systems and differential precession of Mercury
We investigate the applicability of a modified, symplectic leapfrog method with self-adjusted step-size control to the simulations of few-body Hamiltonian systems, and apply it to the direct calculation of differential precession of Mercury due to general relativity and other planets. We calculate the instantaneous differential precession by tracking the Runge-Lenz vector which also enables us to visualize the precession with in-situ real-time animation. We find the modified leadpfrog method to be highly accurate and efficient, and extremely sensitive. We also find the differential precession is non-monotonic, consisting of prograde and retrograde movements leapfrogging each other, with the net precession being prograde. The precession dynamics can be visualized directly with the simple but effective visualization techniques built in the simulation using visual Python.
PrePrint: A multi-scale code for flexible hybrid simulations using ASE framework
Multi-scale computer simulations combine the computationally efficient classical algorithms with more expensive but also more accurate ab-initio quantum mechanical algorithms. This work describes one implementation of multi-scale computations using the Atomistic Simulation Environment (ASE). This implementation can mix classical codes like LAMMPS and the Density Functional Theory-based GPAW. Any combination of codes linked via the ASE interface however can be mixed. We also introduce a framework to easily add classical force fields calculators for ASE using LAMMPS, which also allows harnessing the full performance of classical-only molecular dynamics. Our work makes it possible to combine different simulation codes, quantum mechanical or classical, with great ease and minimal coding effort.