计算金融学术速递[1.10]
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cs.CE计算金融，共计2篇
【1】 Automated Dissipation Control for Turbulence Simulation with Shell Models
标题：壳模型湍流模拟中的自动耗散控制
链接：https://arxiv.org/abs/2201.02485
摘要：The application of machine learning (ML) techniques, especially neural
networks, has seen tremendous success at processing images and language. This
is because we often lack formal models to understand visual and audio input, so
here neural networks can unfold their abilities as they can model solely from
data. In the field of physics we typically have models that describe natural
processes reasonably well on a formal level. Nonetheless, in recent years, ML
has also proven useful in these realms, be it by speeding up numerical
simulations or by improving accuracy. One important and so far unsolved problem
in classical physics is understanding turbulent fluid motion. In this work we
construct a strongly simplified representation of turbulence by using the
Gledzer-Ohkitani-Yamada (GOY) shell model. With this system we intend to
investigate the potential of ML-supported and physics-constrained small-scale
turbulence modelling. Instead of standard supervised learning we propose an
approach that aims to reconstruct statistical properties of turbulence such as
the self-similar inertial-range scaling, where we could achieve encouraging
experimental results. Furthermore we discuss pitfalls when combining machine
learning with differential equations.
【2】 A SIMD algorithm for the detection of epistatic interactions of any order
标题：检测任意阶上位性相互作用的SIMD算法
链接：https://arxiv.org/abs/2201.02460
备注：Submitted to Future Generation Computer Systems. Codes used are available at this https URL
摘要：Epistasis is a phenomenon in which a phenotype outcome is determined by the
interaction of genetic variation at two or more loci and it cannot be
attributed to the additive combination of effects corresponding to the
individual loci. Although it has been more than 100 years since William Bateson
introduced this concept, it still is a topic under active research. Locating
epistatic interactions is a computationally expensive challenge that involves
analyzing an exponentially growing number of combinations. Authors in this
field have resorted to a multitude of hardware architectures in order to speed
up the search, but little to no attention has been paid to the vector
instructions that current CPUs include in their instruction sets. This work
extends an existing third-order exhaustive algorithm to support the search of
epistasis interactions of any order and discusses multiple SIMD implementations
of the different functions that compose the search using Intel AVX Intrinsics.
Results using the GCC and the Intel compiler show that the 512-bit explicit
vector implementation proposed here performs the best out of all of the other
implementations evaluated. The proposed 512-bit vectorization accelerates the
original implementation of the algorithm by an average factor of 7 and 12, for
GCC and the Intel Compiler, respectively, in the scenarios tested.
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