Paul J. Atzberger | Research Group
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Talks: Videos and Slides


Slides:


Videos

Click on the link to video to see full screen versions.


Surface Fluctuating Hydrodynamics Methods: Soft Materials with Fluid-Structure Interactions within Curved Fluid Interfaces, WCCM Conference Talk, Biomechanics and Mechanobiology Session, Nov 2020.

[link to video] [PDF] [GoogleSlides]


The Hidden Role of Mathematics and Computation in Scientific Discovery and Engineering
UCSB Seminar Series: Groundbreaking Research / Innovative Technology (GRIT), July 2016.

Public lecture aimed at general audiences.

[link to video] [slides PDF]


Fluctuating Hydrodynamics Approaches for Lipid Bilayer Membranes
UCLA IPAM Talk at Workshop on Partial Order: Mathematics, Simulations and Applications, January 2016.

[link to video] [slides PDF] [link to UCLA IPAM Workshop] [software]


Fluctuating Hydrodynamics Approaches for Mesoscopic Modeling and Simulation (Part 1)
Stanford University: Summer School on Multiscale Modeling of Materials Workshop, June 2016.

[link to video] [slides PDF] [link to Stanford MMS Workshop] [software]


Fluctuating Hydrodynamics Approaches for Mesoscopic Modeling and Simulation (Part 2)
Stanford University: Summer School on Multiscale Modeling of Materials Workshop, June 2016.

[link to video] [slides PDF] [link to Stanford MMS Workshop]


Tutorial for Mango-Selm Package: How to Setup the Models and Simulations using Jupyter Notebooks and Python.
Interactive Session

Download Mango-Selm Package


Additional videos are available on the Atzberger YouTube Channel and gallery page.



For more information on my current research please see the publications section or the main homepage.


Course Notes and Supplemental Materials

Data Science and Machine Learning

Slides: Statistical Learning Theory, Generalization Errors, and Sampling Complexity Bounds: [Large Slides][PDF] [MicrosoftSlides]

Slides: Complexity Measures, Radamacher, VC-Dimension: [Large Slides] [PDF] [MicrosoftSlides]

Slides: Support Vector Machines, Kernels, Optimization Theory Basics: [Large Slides] [PDF] [MicrosoftSlides]

Slides: Regression, Kernel Methods, Regularization, LASSO, Tomography Example: [Large Slides] [PDF] [MicrosoftSlides]

Slides: Unsupervised Learning, Dimension Reduction, Manifold Learning: [Large Slides] [PDF] [MicrosoftSlides]

Slides: Neural Networks and Deep Learning Basics: [PDF] [GoogleSlides]

Slides: Convolutional Neural Networks (CNNs) Basics: [PDF] [GoogleSlides]

Slides: Recurrent Neural Networks (RNNs) Basics: [Large Slides] [PDF] [MicrosoftSlides]

Slides: Generative Adversarial Networks (GANs): [Large Slides] [PDF] [MicrosoftSlides]

Image Classification using Convolutional Neural Networks (course exercise)

Facial Recognition and Feature Extraction (course exercise)

Machine Learning Course Link

Machine Learning: Foundations and Applications Course (MATH CS 120) [course-link]
Machine Learning: Foundations and Applications Course (MATH 260J) [course-link]

Finite Element Methods: Slides

Non-linear Optimization: Notes

Monte-Carlo Methods

Mathematical Finance

Dynamical Systems and ODEs

The specific parameters are ` delta=0.25, gamma=0.3, omega=1.0 `.






For more information on my current research please see the publications section or the main homepage. Additional videos are also available on the Atzberger YouTube Channel.


Publications | Course Webpages

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