YoVDO

Data to Differential Equations: Discovering Mathematical Models for Biological Systems

Offered By: MICDE University of Michigan via YouTube

Tags

Partial Differential Equations Courses Mechanical Engineering Courses Machine Learning Courses Computational Fluid Dynamics Courses Mathematical Modeling Courses Biological Systems Courses Scientific Computing Courses Parameter Estimation Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a 31-minute conference talk by Liz Livingston, a PhD candidate in Mechanical Engineering and Scientific Computing at the University of Michigan, on discovering mathematical models for biological systems. Delve into the challenges of modeling complex biological phenomena using partial differential equations (PDEs) and learn about innovative approaches to equation discovery. Discover how data-driven methods, including machine learning and inference techniques, can be used to identify governing equations without oversimplifying the system. Gain insights into applications of these tools in complex biological systems, such as flow through stenosed arteries and soft tissue fractures. Understand the speaker's PhD thesis goal of developing and improving mathematical methods to enhance our understanding of intricate biological systems.

Syllabus

MICDE DISCOVER Mini-symposium 2023 - Liz Livingston


Taught by

MICDE University of Michigan

Related Courses

Introduction to Artificial Intelligence
Stanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent