SMU Reading Group

Topics in Applied Math and Data Science 		Version v0.1.0
Updated 2024-08-28
Author Mason A. McCallum, Marc de Vernon License MIT

Introduction

Welcome to the Applied Math Journal Club! The club is open to anyone interested in learning modern topics of applied math. Selected topics for the upcoming semester will include uncertainty quantification, inverse problems, generative modeling in scientific computing, numerics for high-dimensional PDE, and data-driven equation discovery. If you have any questions about the club, please contact us..

Meetings for Fall 2024 will take place in Moody 241 on Tuesdays from 4-5PM.

Fall 2024 Schedule

Topic Discussion Leader Date Supplementary Materials
Welcome Meeting Jimmie Adriazola Sep. 3rd N/A
Math Bio Md Abu Talha Sep. 10th N/A
SympNets Andrew Ho Sep. 24th N/A
KAM Networks Mason McCallum Oct. 1st N/A
Diffusion Models Mason McCallum Oct. 15th N/A
TBD Marc DeVernon Oct. 22nd N/A
Random NLA Jimmie Adriazola Nov 12th N/A
Random NLA Jimmie Adriazola Nov 19th N/A

Past meetings log

Topic Discussion Leader Date Supplementary Materials
Optimal Transport and Applications (Research Talk) Axel Turnquist (UT Austin) April 16, 2024
Flow-based generative models for Markov chain Monte Carlo in lattice field theory Jimmie Adriazola April 9, 2024 Notes, Code
Solving high-dimensional partial differential equations using deep learning Daniel Margolis March 26, 2024 Notes, Slides
Predicting nonlinear dynamics of optical solitons in optical fiber via the SCPINN Sabrina Hetzel March 19, 2024 Code
A Mean-Field Games Laboratory for Generative Modeling Jimmie Adriazola March 5, 2024 Notes
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems Jimmie Adriazola February 27, 2024 Notes
Discovering governing equations from partial measurements with deep delay autoencoders Mason McCallum February 13, 2024 Notes/Code
Discovering governing equations from partial measurements with deep delay autoencoders Mason McCallum February 6, 2024 -
Denoising Diffusion Probabilistic Models Jimmie Adriazola January 23, 2024 Notes
The Bayesian Approach To Inverse Problems Austin Marstaller January 16, 2024 Slides
The Modern Mathematics of Deep Learning, Berner, et al. Jimmie Adriazola December 4, 2023 Notes
The Modern Mathematics of Deep Learning, Berner, et al. Jimmie Adriazola November 20, 2023 Notes
Organizational Meeting N/A October 23, 2023 N/A

Suggested Reading

What Kinds of Functions Do Deep Neural Networks Learn? Insights from Variational Spline Theory

Solving high-dimensional partial differential equations using deep learning

Score-Based Generative Modeling through Stochastic Differential Equations

In-context operator learning with data prompts for differential equation problems

Approximation rates for neural networks with general activation functions

Acknowledgments

I would like to acknowledge Jimmie Adriazola for mentoring and guiding the formation of this reading group. Marc Devernon for managing the webpage. This webpage is a fork of: https://github.com/owickstrom/the-monospace-web.git So much thanks to Oskar Wickstrom for the nice work