## Talks: Videos and Slides

For latest talks and videos see: youtube:atzberger.

**Deep Learning Approaches for Non-linear Dynamics: Generative Approaches for Data-Driven Simulations,**MiC Seminar, DOE ORNL [Video]**Generative Machine Learning Approaches for Data-Driven Modeling of Dynamics,**DDPS Seminar, DOE LLNL [Video]**Protein Drift-Diffusion in Curved Membranes: Surface Fluctuating Hydrodynamics Methods**, APS March Meeting Talk (biophysics session),[Video]**Surface Fluctuating Hydrodynamics Methods: Soft Materials with Fluid-Structure Interactions within Curved Fluid Interfaces**, World Congress in Computational Mechanics (WCCM) Talk, [PDF] [GoogleSlides] [Video]**Stochastic Immersed Boundary Methods for Fluid-Structure Interactions,**New York University (NYU), [PDF] [Video]**Learning Nonlinear Dynamics of Physical Systems: Geometric Dynamic Variational Autoencoders (GD-VAEs)**, [Video]**SELM: Software Tutorial: Fluid-Structure Interaction Simulations,**LAMMPS Workshop, [Video]**Regression, Kernel Methods, Regularization, LASSO, Tomography Example,**Machine Learning Lecture, [PDF] [MicrosoftSlides] [Video (Part 1)] [Video (Part 2)]**MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS (Overview),**LAMMPS Workshop, [Video]**Statistical Learning Theory, Generalization Errors, and Sampling Complexity Bounds,**Machine Learning Lecture, [PDF] [MicrosoftSlides]**Complexity Measures, Radamacher, VC-Dimension,**Machine Learning Lecture, [PDF] [MicrosoftSlides]**Support Vector Machines, Kernels, Optimization Theory Basics,**Machine Learning Lecture, [PDF] [MicrosoftSlides]**Unsupervised Learning, Dimension Reduction, Manifold Learning**, Machine Learning Lecture, [PDF] [MicrosoftSlides]**Neural Networks and Deep Learning Basics**, Machine Learning Lecture, [PDF] [GoogleSlides]**Convolutional Neural Networks (CNNs) Basics**, Machine Learning Lecture, [PDF] [GoogleSlides]**Recurrent Neural Networks (RNNs) Basics**, Machine Learning Lecture, [PDF] [MicrosoftSlides]**Generative Adversarial Networks (GANs)**, Machine Learning Lecture, [PDF] [MicrosoftSlides]**Learning Nonlinear Dynamics of Physical Systems: Variational Autoencoders (VAEs)**, ML-DL Workshop, Sandia National Laboratories, July 2021, [Video]**GRIT Talk**(public lecture aimed at general audiences, see video below), [PDF] [Video]**UCLA IPAM Talk**at Workshop on Partial Order: Mathematics, Simulations and Applications, [PDF] [GoogleSlides][Video]**Fluctuating Hydrodynamics Approaches for Mesoscopic Modeling and Simulation (Part 1)**, Stanford University: Summer School on Multiscale Modeling of Materials Workshop, [PDF] [GoogleSlides] [Video]**Fluctuating Hydrodynamics Approaches for Mesoscopic Modeling and Simulation (Part 2)**, Stanford University: Summer School on Multiscale Modeling of Materials Workshop, [PDF] [GoogleSlides] [Video]

## Videos

For latest talks and videos see

Atzberger YouTube Channel

**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.

**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] [software]

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

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

Fluid-Structure Interaction Modeling and Simulation

**Lecture:** Regression, Kernel Methods, Regularization, LASSO, Tomography Example: [PDF] [MicrosoftSlides]

[link to video (part 1)] [link to video (part 2)]

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:

[MicrosoftSlides] [PDF]

**Slides:** Complexity Measures, Radamacher, VC-Dimension:

[MicrosoftSlides] [PDF]

**Slides:** Support Vector Machines, Kernels, Optimization Theory Basics:

[MicrosoftSlides] [PDF]

**Slides:** Regression, Kernel Methods, Regularization, LASSO, Tomography Example:

[MicrosoftSlides] [PDF] [Video (Part 1)] [Video (Part 2)]

**Slides:** Unsupervised Learning, Dimension Reduction, Manifold Learning:

[MicrosoftSlides] [PDF]

**Slides:** Neural Networks and Deep Learning Basics:

[GoogleSlides] [PDF]

**Slides:** Convolutional Neural Networks (CNNs) Basics:

[GoogleSlides]
[PDF]

**Slides:** Recurrent Neural Networks (RNNs) Basics:

[MicrosoftSlides]
[PDF]

**Slides:** Generative Adversarial Networks (GANs):

[MicrosoftSlides]
[PDF]

Image Classification using Convolutional Neural Networks (course exercise)

Facial Recognition and Feature Extraction (course exercise)

- [Jupyter Notebook PDF]
- [Jupyter Notebook Code]
- [data-folder]
- [Kaggle: Facial Recognition (SVM)]
- [Kaggle PDF]

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 **

- Introduction to FEM and Ritz-Galerkin Approximation [PDF] [GoogleSlides]
- Sobolev Spaces [PDF] [GoogleSlides]
- Finite Element Spaces [PDF] [GoogleSlides]
- Finite Element Approximation Properties and Convergence [PDF] [GoogleSlides]
- Variational Formulation of Elliptic PDEs [PDF] [GoogleSlides]
- Elasticity Theory [PDF] [GoogleSlides]
- Finite Element Mixed Methods [PDF] [GoogleSlides]

** Non-linear Optimization: Notes **

** Monte-Carlo Methods **

** Mathematical Finance **

- An Introduction to Portfolio Theory [PDF]
- The Black-Scholes-Merton Approach to Pricing Options [PDF]
- Contingent Claims and the Arbitrage Theorem [PDF]
- A Brief Introduction to Stochastic Volatility Modeling [PDF]

Partial Differential Equations: Course Notes:
| ||

(please submit any typos here) | ||

- Method of Characteristics, Solving First-Order PDEs | [PDF] | |

- Classifying Second-Order PDEs and Canonical Forms | [PDF] | |

- Wave Equation and Solution Techniques | [PDF] | |

- Diffusion Equation and Solution Techniques | [PDF] | |

- Separation of Variables | [PDF] | |

- Fourier Methods | [PDF] | |

- Elliptic PDEs and Fourier Approaches | [PDF] | |

- Discrete Fourier Transforms (DFTs) and Approximate Solutions of PDEs | [PDF] | |

- Finite Difference Methods and von Neumann Analysis | [PDF] | |

** Dynamical Systems and ODEs **

- Poincare Sections of the Duffing Oscillator: [link to video]

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.