105 citations · 133 across the 16 of their papers we have counts for
19 papers
Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speed
Harrison J. Goldwyn, Mitchell Krock, Johann Rudi +2
Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While exis…
Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets
Arkaprabha Ganguli, Nesar Ramachandra, Julie Bessac +1
This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigat…
Constructing Level Sets Using Smoothed Approximate Bayesian Computation
David Edwards, Julie Bessac, Franck Cappello +1
This paper presents a novel approach to level set estimation for any function/simulation with an arbitrary number of continuous inputs and arbitrary numbers of continuous responses…
A Framework for Balancing Power Grid Efficiency and Risk with Bi-objective Stochastic Integer Optimization
Ramsey Rossmann, Mihai Anitescu, Julie Bessac +4
Power grid expansion planning requires making large investment decisions in the present that will impact the future cost and reliability of a system exposed to wide-ranging uncerta…
Modeling spatial asymmetries in teleconnected extreme temperatures
Mitchell L. Krock, Julie Bessac, Michael L. Stein
Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., envir…
Black-Box Statistical Prediction of Lossy Compression Ratios for Scientific Data
Robert Underwood, Julie Bessac, David Krasowska +3
Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and mov…
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification
Daniel Getter, Julie Bessac, Johann Rudi +1
Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over re…
Joint modeling of wind speed and wind direction through a conditional approach
Eva Murphy, Whitney Huang, Julie Bessac +2
Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to cl…
Station-wise statistical joint assessment of wind speed and direction under future climates across the United States
Qiuyi Wu, Julie Bessac, Whitney Huang +1
This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the representativ…
Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets
David Krasowska, Julie Bessac, Robert Underwood +3
Lossy compression plays a growing role in scientific simulations where the cost of storing their output data can span terabytes. Using error bounded lossy compression reduces the a…
Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails
Mitchell Krock, Julie Bessac, Michael L. Stein +1
In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values…
Neural Networks for Parameter Estimation in Intractable Models
Amanda Lenzi, Julie Bessac, Johann Rudi +1
We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. We show how to estimate par…
SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors
Kai Zhao, Sheng Di, Xin Liang +5
Efficient error-controlled lossy compressors are becoming critical to the success of today's large-scale scientific applications because of the ever-increasing volume of data produ…
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE
Johann Rudi, Julie Bessac, Amanda Lenzi
Machine learning algorithms have been successfully used to approximate nonlinear maps under weak assumptions on the structure and properties of the maps. We present deep neural net…
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms
Emil M. Constantinescu, Noemi Petra, Julie Bessac +1
This paper introduces a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution repres…
Forecast score distributions with imperfect observations
Julie Bessac, Philippe Naveau
The classical paradigm of scoring rules is to discriminate between two different forecasts by comparing them with observations. The probability distribution of the observed record…
Global sensitivity analysis for statistical model parameters
Joseph Hart, Julie Bessac, Emil Constantinescu
Global sensitivity analysis (GSA) is frequently used to analyze the influence of uncertain parameters in mathematical models and simulations. In principle, tools from GSA may be ex…
Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical models
Julie Bessac, Emil Mihai Constantinescu, Mihai Anitescu
We propose a statistical space-time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaus…
Gaussian linear state-space model for wind fields in the North-East Atlantic
Julie Bessac, Pierre Ailliot, Valerie Monbet
A space-time model for wind fields is proposed. It aims at simulating realistic wind conditions with a focus on reproducing the space-time motions of the meteorological systems. A…