Presented By: Frontiers in Scientific Machine Learning (FSML)
Frontiers in Scientific Machine Learning Seminar 13: Statistical learning for Summary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution
Julie Bessac (National Renewable Energy Laboratory)
This is a hybrid seminar. Attendees can join online or at 2636 GGB (Breakfast and coffee will be provided)
Link to join via Zoom: https://umich.zoom.us/j/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1
Abstract:
In the first part of this talk, we will discuss summary statistics of physics-based model outputs and their correction with observational data.
Physics-based models capture broad-scale dynamics across various spatial and temporal scales, they often face challenges such as modeling biases, high computational costs, along with large outputs that are challenging to manipulate. On the other hand, observations capture localized variability but are typically sparse. This talk presents an innovative approach to address these challenges by utilizing summary statistics from physics-based model outputs and enhancing them with observational information via neural networks.
In the second part of the talk, we will present neural networks with closed-form probabilistic loss that applied to super-resolution of surface wind speed. We will illustrate that the use of a closed-form probabilistic loss provides the neural network with a sampling capability and a spatial covariance for super-resolved wind fields.
These are joint work with Atlanta Chakraborty (NREL), Harrison Goldwyn (NREL), Daniel Getter (USC), Johann Rudi (Virginia Tech) and Mitchell Krock (University of Missouri)
Bio: Julie Bessac received her Ph.D. degree in 2014 in Applied Mathematics from the University of Rennes 1, France. Between 2014 and 2023, she was a post-doctoral appointee and a research scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. She joined National Renewable Energy Laboratory in 2023 as a computational statistician. She is an adjunct professor at the Department of Statistics at Virginia Tech. Her research focuses on statistical and machine learning methods for modeling, forecasting and uncertainty quantification for diverse applications: geophysical processes and their applications to energy systems, computer science and nuclear physics.
Link to join via Zoom: https://umich.zoom.us/j/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1
Abstract:
In the first part of this talk, we will discuss summary statistics of physics-based model outputs and their correction with observational data.
Physics-based models capture broad-scale dynamics across various spatial and temporal scales, they often face challenges such as modeling biases, high computational costs, along with large outputs that are challenging to manipulate. On the other hand, observations capture localized variability but are typically sparse. This talk presents an innovative approach to address these challenges by utilizing summary statistics from physics-based model outputs and enhancing them with observational information via neural networks.
In the second part of the talk, we will present neural networks with closed-form probabilistic loss that applied to super-resolution of surface wind speed. We will illustrate that the use of a closed-form probabilistic loss provides the neural network with a sampling capability and a spatial covariance for super-resolved wind fields.
These are joint work with Atlanta Chakraborty (NREL), Harrison Goldwyn (NREL), Daniel Getter (USC), Johann Rudi (Virginia Tech) and Mitchell Krock (University of Missouri)
Bio: Julie Bessac received her Ph.D. degree in 2014 in Applied Mathematics from the University of Rennes 1, France. Between 2014 and 2023, she was a post-doctoral appointee and a research scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. She joined National Renewable Energy Laboratory in 2023 as a computational statistician. She is an adjunct professor at the Department of Statistics at Virginia Tech. Her research focuses on statistical and machine learning methods for modeling, forecasting and uncertainty quantification for diverse applications: geophysical processes and their applications to energy systems, computer science and nuclear physics.
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