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Machine Learning Approaches for Atmospheric and Material Fracture Applications and their Uncertainty Quantification

My background

Why Machine Learning (ML) Approaches?

Why Uncertainty Quantification (UQ) in ML?

Outline for this presentation

Problem of Interest and Motivation

Description of the Data

Overview of K-Nearest Neighbors Approach

KNN Graphical Examples

Our Enhanced KNN-based Approach

The KNN-based Prediction

Performance Metrics Options the number of neighbors used

Figure of Merit in the Space (FMS)

Normalized Root Mean Squared Error (NRMSE)

Fraction of Data (FAC2)

Fractional Bias

Coefficient of Determination, Slope and Intercept

Summary: KNN Approach

Summary: Performance Metrics

Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture [1]

Multivariate Neural Networks Model

Heteroscedastic Training Loss Function

Multivariate Heteroscedastic Approach

Description:

Explore machine learning approaches for predicting seismic, acoustic, and atmospheric data in this 41-minute conference talk from the Fields Institute. Delve into enhanced K-Nearest Neighbors (KNN) techniques and their applications in atmospheric and material fracture studies. Learn about the importance of uncertainty quantification in machine learning and various performance metrics for evaluating predictions. Examine multivariate neural networks and heteroscedastic training loss functions for high-strain brittle fracture analysis. Gain insights from M. Giselle Fernández-Godino of Lawrence Livermore National Laboratory on cutting-edge methods for controlling error and improving efficiency in numerical models across diverse scientific domains.

Machine Learning Approaches for Predicting Seismic, Acoustic and Atmospheric Data

Fields Institute

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#Computer Science #Machine Learning #Mathematics #Statistics & Probability #Uncertainty Quantification #K-Nearest Neighbors