In this short course, we will introduce foundations of computational problem solving for mathematical models described by deterministic and stochastic differential equation systems. We start with an introductory course that will focus on foundations of computational problem solving with an overview on various state-of-the-art tools and techniques to solve a variety of applications. The second part will introduce diverse approaches for estimating parameters from real-data sets for prediction and control for deterministic problems modeling COVID-19 dynamics. These approaches will include standard numerical optimization approaches to disease informed neural network using PINNs and other Machine Learning approaches. The final part of the course will extend the applications to incorporating stochastic dynamics into the governing differential equations that describe COVID-19 dynamics. Participants will have the opportunity to work on a variety of software platforms during this three part course that includes:

• Day 1: Foundations of Computational Problem Solving: Tools and Techniques
• Day 2: Mathematical Models for Infectious Disease Dynamics: Prediction and Control

Lesson	Notebook
Computational Thinking
Machine Learning Overview
Regression
Model Selection
Neural Networks
Diseases-Informed Neural Networks