YoVDO

UQ for ML and ML for UQ - Uncertainty Quantification and Machine Learning in Physics-Based Modeling

Offered By: MICDE University of Michigan via YouTube

Tags

Uncertainty Quantification Courses Machine Learning Courses Computational Modeling Courses Scientific Machine Learning Courses Physics Informed Machine Learning Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the interrelated roles of Uncertainty Quantification (UQ) and Machine Learning (ML) in physics-based computational modeling through this 57-minute seminar by Michael D. Shields, Associate Professor of Civil & Systems Engineering at Johns Hopkins University. Delve into the concepts of "UQ for ML" and "ML for UQ" and their significance in modern physics-based computational modeling paradigms. Examine how these approaches are applied in various fields, from multi-scale materials modeling to high energy-density physics. Gain insights into the importance of addressing uncertainties in parameters, inputs, and model structures when developing physics-based models and implementing scientific machine learning methods.

Syllabus

Michael Shields: UQ for ML and ML for UQ


Taught by

MICDE University of Michigan

Related Courses

Introduction to Scientific Machine Learning
Purdue University via edX
Scientific Machine Learning: Opportunities and Challenges - Keynote
The Julia Programming Language via YouTube
Scientific Machine Learning - Where Physics-based Modeling Meets Data-driven Learning
Santa Fe Institute via YouTube
AI for Science - Expo Stage Talk - AAAS Annual Meeting
AAAS Annual Meeting via YouTube
Leveraging Physics-Induced Bias in Scientific Machine Learning for Computational Mechanics
Alan Turing Institute via YouTube