Note: the session will be recorded for registrants who cannot attend at the scheduled time.

In the rapidly evolving landscape of data-driven modeling, the ability to refine and adjust parameters in differential equations using empirical data is becoming increasingly crucial. This tech talk delves into advanced techniques for enhancing the accuracy and predictive power of differential equation models from physics, biology, and engineering. Through a series of interactive Python exercises, participants will learn how to implement these techniques using popular Python libraries and MATLAB

Participants should feel free to bring a laptop and follow along. Software used in this talk can be accessed from the links below:

• Python: www.python.org/downloads/
• Jupyter Notebook: docs.jupyter.org/en/latest/install/notebook-classic.html 
• MATLAB: can be accessed by U of I community members via the University's VLAB (instructions here)