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1
Intro
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Underlying Concept
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// Example Problem
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Example Application: Turbulent Data Compression
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Example Application: Sparse Sensor Placement
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NIF is Mesh Agnostic
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Results/Benchmark Data
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// Growing Vortices/ Cool Pictures
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Shape Net Architectures
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Outro
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the concept of Neural Implicit Flow (NIF) in this 14-minute video lecture on Physics Informed Machine Learning. Delve into the underlying principles and discover practical applications such as turbulent data compression and sparse sensor placement. Learn about the mesh-agnostic nature of NIF and examine benchmark results. Gain insights into Shape Net architectures and their relevance to the field. Produced at the University of Washington with funding support from the Boeing Company, this informative presentation offers a comprehensive overview of NIF and its potential impact on physics-based machine learning.

Neural Implicit Flow - Physics Informed Machine Learning

Steve Brunton
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