11 research outputs found
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
We present a study for the generation of events from a physical process with
deep generative models. The simulation of physical processes requires not only
the production of physical events, but also to ensure these events occur with
the correct frequencies. We investigate the feasibility of learning the event
generation and the frequency of occurrence with Generative Adversarial Networks
(GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo
generators. We study three processes: a simple two-body decay, the processes
and including the decay of the top
quarks and a simulation of the detector response. We find that the tested GAN
architectures and the standard VAE are not able to learn the distributions
precisely. By buffering density information of encoded Monte Carlo events given
the encoder of a VAE we are able to construct a prior for the sampling of new
events from the decoder that yields distributions that are in very good
agreement with real Monte Carlo events and are generated several orders of
magnitude faster. Applications of this work include generic density estimation
and sampling, targeted event generation via a principal component analysis of
encoded ground truth data, anomaly detection and more efficient importance
sampling, e.g. for the phase space integration of matrix elements in quantum
field theories.Comment: 24 pages, 10 figure
Predicting atmospheric optical properties for radiative transfer computations using neural networks
The radiative transfer equations are well-known, but radiation
parametrizations in atmospheric models are computationally expensive. A
promising tool for accelerating parametrizations is the use of machine learning
techniques. In this study, we develop a machine learning-based parametrization
for the gaseous optical properties by training neural networks to emulate a
modern radiation parameterization (RRTMGP). To minimize computational costs, we
reduce the range of atmospheric conditions for which the neural networks are
applicable and use machine-specific optimised BLAS functions to accelerate
matrix computations. To generate training data, we use a set of randomly
perturbed atmospheric profiles and calculate optical properties using RRTMGP.
Predicted optical properties are highly accurate and the resulting radiative
fluxes have average errors within \SI{0.5}{\flux} compared to RRTMGP. Our
neural network-based gas optics parametrization is up to 4 times faster than
RRTMGP, depending on the size of the neural networks. We further test the
trade-off between speed and accuracy by training neural networks for the narrow
range of atmospheric conditions of a single large-eddy simulation, so smaller
and therefore faster networks can achieve a desired accuracy. We conclude that
our machine learning-based parametrization can speed-up radiative transfer
computations whilst retaining high accuracy.Comment: 13 pages,5 figures, submitted to Philosophical Transactions
NetSquid, a NETwork Simulator for QUantum Information using Discrete events
In order to bring quantum networks into the real world, we would like to
determine the requirements of quantum network protocols including the
underlying quantum hardware. Because detailed architecture proposals are
generally too complex for mathematical analysis, it is natural to employ
numerical simulation. Here we introduce NetSquid, the NETwork Simulator for
QUantum Information using Discrete events, a discrete-event based platform for
simulating all aspects of quantum networks and modular quantum computing
systems, ranging from the physical layer and its control plane up to the
application level. We study several use cases to showcase NetSquid's power,
including detailed physical layer simulations of repeater chains based on
nitrogen vacancy centres in diamond as well as atomic ensembles. We also study
the control plane of a quantum switch beyond its analytically known regime, and
showcase NetSquid's ability to investigate large networks by simulating
entanglement distribution over a chain of up to one thousand nodes.Comment: NetSquid is freely available at https://netsquid.org; refined main
text section
Less is not more: We need rich datasets to explore
Traditional datacenter analysis is based on high-level, coarse-grained metrics. This obscures our vision of datacenter behavior, as we do not observe the full picture nor subtleties that might make up these high-level, coarse metrics. There is room for operational improvement based on fine-grained temporal and spatial, low-level metric data. We leverage in this work one of the (rare) public datasets providing fine-grained information on datacenter operations, with over 60 billion measurements captured in 15-second intervals. We show evidence that fine-grained information reveals new operational aspects, that the different metrics cannot be derived from one another (and thus need to be captured), and that many low-level metrics, gathered frequently are key to understanding datacenter operations. We propose a holistic analysis for datacenter operations, providing statistical characterization of node and workload aspects. Our analysis reveals both generic and machine learning-specific aspects, summarized in over 30 observations, providing deep insight into this dataset and the originating cluster. We give actionable insights, surprising findings, and exemplify how our observations support performance-engineering tasks such as workload prediction and long-term datacenter design
Multi_Scale_Tools ::a Python library to exploit multi-scale whole slide images
Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. Tissue samples are scanned at a high-resolution and usually saved as images with several magnification levels, namely whole slide images (WSIs). Convolutional neural networks (CNNs) represent the state-of-the-art computer vision methods targeting the analysis of histopathology images, aiming for detection, classification and segmentation. However, the development of CNNs that work with multi-scale images such as WSIs is still an open challenge. The image characteristics and the CNN properties impose architecture designs that are not trivial. Therefore, single scale CNN architectures are still often used. This paper presents Multi_Scale_Tools, a library aiming to facilitate exploiting the multi-scale structure of WSIs. Multi_Scale_Tools currently include four components: a pre-processing component, a scale detector, a multi-scale CNN for classification and a multi-scale CNN for segmentation of the images. The pre-processing component includes methods to extract patches at several magnification levels. The scale detector allows to identify the magnification level of images that do not contain this information, such as images from the scientific literature. The multi-scale CNNs are trained combining features and predictions that originate from different magnification levels. The components are developed using private datasets, including colon and breast cancer tissue samples. They are tested on private and public external data sources, such as The Cancer Genome Atlas (TCGA). The results of the library demonstrate its effectiveness and applicability. The scale detector accurately predicts multiple levels of image magnification and generalizes well to independent external data. The multi-scale CNNs outperform the single-magnification CNN for both classification and segmentation tasks. The code is developed in Python and it will be made publicly available upon publication. It aims to be easy to use and easy to be improved with additional functions
Development of a large-eddy simulation subgrid model based on artificial neural networks : A case study of turbulent channel flow
Atmospheric boundary layers and other wall-bounded flows are often simulated with the large-eddy simulation (LES) technique, which relies on subgrid-scale (SGS) models to parameterize the smallest scales. These SGS models often make strong simplifying assumptions. Also, they tend to interact with the discretization errors introduced by the popular LES approach where a staggered finite-volume grid acts as an implicit filter. We therefore developed an alternative LES SGS model based on artificial neural networks (ANNs) for the computational fluid dynamics MicroHH code (v2.0). We used a turbulent channel flow (with friction Reynolds number ReτCombining double low line590) as a test case. The developed SGS model has been designed to compensate for both the unresolved physics and instantaneous spatial discretization errors introduced by the staggered finite-volume grid. We trained the ANNs based on instantaneous flow fields from a direct numerical simulation (DNS) of the selected channel flow. In general, we found excellent agreement between the ANN-predicted SGS fluxes and the SGS fluxes derived from DNS for flow fields not used during training. In addition, we demonstrate that our ANN SGS model generalizes well towards other coarse horizontal resolutions, especially when these resolutions are located within the range of the training data. This shows that ANNs have potential to construct highly accurate SGS models that compensate for spatial discretization errors. We do highlight and discuss one important challenge still remaining before this potential can be successfully leveraged in actual LES simulations: we observed an artificial buildup of turbulence kinetic energy when we directly incorporated our ANN SGS model into a LES simulation of the selected channel flow, eventually resulting in numeric instability. We hypothesize that error accumulation and aliasing errors are both important contributors to the observed instability. We finally make several suggestions for future research that may alleviate the observed instability. </p
NetSquid, a NETwork Simulator for QUantum Information using Discrete events
In order to bring quantum networks into the real world, we would like to determine the requirements of quantum network protocols including the underlying quantum hardware. Because detailed architecture proposals are generally too complex for mathematical analysis, it is natural to employ numerical simulation. Here we introduce NetSquid, the NETwork Simulator for QUantum Information using Discrete events, a discrete-event based platform for simulating all aspects of quantum networks and modular quantum computing systems, ranging from the physical layer and its control plane up to the application level. We study several use cases to showcase NetSquid’s power, including detailed physical layer simulations of repeater chains based on nitrogen vacancy centres in diamond as well as atomic ensembles. We also study the control plane of a quantum switch beyond its analytically known regime, and showcase NetSquid’s ability to investigate large networks by simulating entanglement distribution over a chain of up to one thousand nodes.QID/Elkouss GroupQuTechBUS/TNO STAFFQID/Wehner GroupBusiness DevelopmentQID/Software GroupElectrical Engineering, Mathematics and Computer ScienceQuantum Internet DivisionQuantum Information and Softwar