Applied Mathematics/Data Science Journal Club

Welcome to the Applied Math Journal Club! The club is open to anyone interested in learning modern topics of applied math. Selected topics so 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 2025 will take place in Moody 241. Meeting time is still TBD.

Past Meeting Information/Materials

Club Schedule: Fall 2023

Topic/Article	Discussion Leader	Date	Supplementary Materials
Organizational Meeting	N/A	October 23, 2023	N/A
The Modern Mathematics of Deep Learning, Berner, et al.	Jimmie Adriazola	November 20, 2023	Notes
The Modern Mathematics of Deep Learning, Berner, et al.	Jimmie Adriazola	December 4, 2023	Notes

Club Schedule: Spring 2024

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

Suggested Future Readings:

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