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Data-Driven Modeling of Unknown Systems with Deep Neural Networks

Offered By: Inside Livermore Lab via YouTube

Tags

Deep Neural Networks Courses Machine Learning Courses Scientific Computing Courses Dynamical Systems Courses Partial Differential Equations Courses Predictive Modeling Courses Uncertainty Quantification Courses

Course Description

Overview

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Explore a framework for predictive modeling of unknown systems using measurement data in this 1 hour 15 minute lecture by Dongbin Xiu. Learn how deep neural networks are employed to discover and approximate unknown evolution operators, enabling system analysis and prediction. Discover the applications of flow map learning (FML) in modeling dynamical systems, systems with missing variables and hidden parameters, and partial differential equations (PDEs). Gain insights from Xiu's extensive research background in uncertainty quantification, stochastic computing, and machine learning as he presents this innovative approach to data-driven modeling.

Syllabus

DDPS | Data-driven modeling of unknowns systems with deep neural networks by Dongbin Xiu


Taught by

Inside Livermore Lab

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