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MODELING NUCLEAR DATA UNCERTAINTIES USING DEEP NEURAL NETWORKS
2021
EPJ Web of Conferences
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A new concept using deep learning in neural networks is investigated to characterize the underlying uncertainty of nuclear data. ...
Although of the very limited sample size (1000 samples) available in this study, the trained models demonstrate promising performance, where a prediction error of about 166 pcm is found for keff in the ...
Next, Sampler receives the "pre-determined" cross-section libraries and uses them for forward uncertainty propagation to determine the effect of nuclear data uncertainty on the output. ...
doi:10.1051/epjconf/202124715016
fatcat:pg7dtgxbmfdyjijqizkkirjyom
Deep Horizon; a machine learning network that recovers accreting black hole parameters
[article]
2019
arXiv
pre-print
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We train two convolutional deep neural networks on a large image library of simulated mock data. ...
We investigate the effects of a limited telescope resolution and of observations at different frequencies. ...
This work was funded by the ERC Synergy Grant "BlackHoleCam-Imaging the Event Horizon of Black Holes" (Grant 610058, Goddi et al. (2017) ). The ...
arXiv:1910.13236v1
fatcat:hry74ga5wfgqtelwrenzesissm
Deep Horizon: A machine learning network that recovers accreting black hole parameters
2020
Astronomy and Astrophysics
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We trained two convolutional deep neural networks on a large image library of simulated mock data. ...
We investigate the effects of a limited telescope resolution and observations at higher frequencies. Methods. ...
This work was funded by the ERC Synergy Grant A94, page 11 of 12 A&A 636, A94 (2020) "BlackHoleCam-Imaging the Event Horizon of Black Holes" (Grant 610058, Goddi et al. 2017) . ...
doi:10.1051/0004-6361/201937014
fatcat:4vyvbfxdlzbrdgppicmvtumgwm
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
[article]
2021
arXiv
pre-print
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Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. ...
TensorFlow Probability is a library for probabilistic modeling and inference which can be used for both approaches of probabilistic deep learning. We include its code examples for illustration. ...
Then, one has to choose a probability model: a prior distribution over the possible model parameters p(θ) and a prior confidence in the predictive power of the model p(y | x, θ), i.e., BNN θ (x). ...
arXiv:2106.00120v3
fatcat:gbeonxch4vav7jaqu3nvti7thi
Estimating atmospheric parameters from LAMOST low-resolution spectra with low SNR
2022
Monthly notices of the Royal Astronomical Society
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2022 pre-print arXiv:2207.06042v1 fatcat:wmyadgnanjbypaegfemkjwp2j4
The effectiveness of the LASSO-MLPNet was evaluated on some LAMOST stellar spectra of the common star between APOGEE (The Apache Point Observatory Galactic Evolution Experiment) and LAMOST. it is shown ...
Finally, LASSO-MLPNet used a Multilayer Perceptron network (MLPNet) to estimate atmospheric parameters Teff, log g and [Fe/H]. ...
LAMOST, a multi-target optical fiber spectroscopic telescope in the large sky area, is a major national engineering project built by the Chinese Academy of Sciences. ...
doi:10.1093/mnras/stac1625
fatcat:ar2f2ygxirfkpb7iulmh3mrls4
Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
[article]
2022
arXiv
pre-print
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In this work, we demonstrate that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty quantification, of such morphological parameters from simulated low-surface-brightness ...
Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different galaxy populations. ...
We acknowledge the Deep Skies Lab as a community of multi-domain experts and collaborators who've facilitated an environment of open discussion, idea-generation, and collaboration. ...
arXiv:2207.03471v1
fatcat:6mvrd5suxvbijmr5c2fy4dxwdu
Epistemic Neural Networks
[article]
2022
arXiv
pre-print
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While prior approaches to uncertainty modeling such as Bayesian neural networks can be expressed as ENNs, this new interface facilitates comparison of joint predictions and the design of novel architectures ...
We introduce the epistemic neural network (ENN) as an interface for models that represent uncertainty as required to generate useful joint predictions. ...
and code with the Uncertainty Baselines project (Nado et al., 2021) . ...
arXiv:2107.08924v5
fatcat:gaiua6m5vbckvbpmsv2vysxd2u
TyXe: Pyro-based Bayesian neural nets for Pytorch
[article]
2021
arXiv
pre-print
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We introduce TyXe, a Bayesian neural network library built on top of Pytorch and Pyro. ...
We showcase the ease of use of TyXe to explore Bayesian versions of popular models from various libraries: toy regression with a pure Pytorch neural network; large-scale image classification with torchvision ...
We then leverage Pyro to formulate a probabilistic model over the neural network parameters, in which we perform approximate inference. ...
arXiv:2110.00276v1
fatcat:x6f37xqiefcwpjh6qhvscx6shu
SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
[article]
2021
arXiv
pre-print
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These parameters are derived with a flexible data-driven algorithm that uses a neural network model. ...
We present our derived parameters along with an analysis of the uncertainties and comparisons to other analyses from the literature. ...
We train a neural network on this set to reproduce a spectrum as a function of its parameters, then use the network to determine the parameters of the remaining spectra in the library. ...
arXiv:2112.01669v1
fatcat:jyjvvdtulbcx7ms44dibwcmtj4
SDSS-IV MaStar: Data-driven Parameter Derivation for the MaStar Stellar Library
2022
Astronomical Journal
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These parameters are derived with a flexible data-driven algorithm that uses a neural network model. ...
We present our derived parameters along with an analysis of the uncertainties and comparisons to other analyses from the literature. ...
ORCID iDs Julie Imig https:/ /orcid.org/0000-0003-2025-3585 Jon A. ...
doi:10.3847/1538-3881/ac3ca7
fatcat:v5hjzjgvwrbazaqz4qe3uqdu3u
Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models
2021
Advanced Intelligent Systems
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It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance ...
the second is a simulated high-dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). ...
This work was supported by the Agency of Science, Technology and Research (A*STAR), Singapore, via two programmatic research grants (grant nos. A1898b0043 and A19E9a0103 ). ...
doi:10.1002/aisy.202100101
fatcat:tnmlfhgsyven5lgjywk7oexpdq
Addressing biological uncertainties in engineering gene circuits
2016
Integrative Biology
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We discuss biological uncertainties that complicate predictable engineering of gene circuits and potential strategies to address these uncertainties. ...
Quantifying effects of parts on the chassis 2.1 Computational models. Modeling can evaluate the impact of uncertainty on circuit function and the robustness of a circuit against these uncertainties. ...
Specific network motifs can resist effects of noise on circuit function (Fig. 2D ). ...
doi:10.1039/c5ib00275c
pmid:26674800
pmcid:PMC4837052
fatcat:y7ae6c2jufddxn4l4yfqagluly
BNNpriors: A library for Bayesian neural network inference with different prior distributions
2021
Software Impacts
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Our new library, BNNpriors, enables state-of-the-art Markov Chain Monte Carlo inference on Bayesian neural networks with a wide range of predefined priors, including heavy-tailed ones, hierarchical ones ...
It has facilitated foundational discoveries on the nature of the cold posterior effect in Bayesian neural networks and will hopefully catalyze future research as well as practical applications in this ...
Acknowledgments VF was supported by a PhD fellowship from the Swiss Data Science Center and by the grant #2017-110 of the Strategic Focus Area ''Personalized Health and Related Technologies (PHRT)'' of ...
doi:10.1016/j.simpa.2021.100079
fatcat:4da5sth5tbfkpch2chxsd4re2a
Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles
[article]
2020
arXiv
pre-print
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We train our networks on a library of 2.1 million simulated flame videos. ...
Results on the test dataset of simulated flames show that the network recovers flame model parameters, with the correlation coefficient between predicted and true parameters ranging from 0.97 to 0.99, ...
Acknowledgments and Disclosure of Funding This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement number ...
arXiv:2011.02838v1
fatcat:dnes2apumjfs7okrvzaht3ha24
MODELLING OF EQUIPMENT FAILURE RATE ACCOUNTING FOR THE UNCERTAINTY
2015
International Journal on Smart Sensing and Intelligent Systems
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A fuzzy model for failure rate with the consideration of the effects of uncertain factors in distribution reliability evaluation is presented. ...
The possibility and credibility distribution analyzed on the basis of sample datum are used for quantifying effects of the uncertainty done to failure rate. ...
Conclusions A novel model of the equipment failure rate for incorporating and weigh the effects of uncertainty factors in distribution network is proposed. ...
doi:10.21307/ijssis-2017-816
fatcat:bm4em2lhirgovl4txy2jl3e6vq
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