Research Technology Master Calendar

Instructors: Diab Abueidda, Qibang Liu
Date/Time:  Friday, April 10, 2026
Time: 10:00 AM -  12:00 PM Central Time
Location: Zoom (Zoom coordinates will be provided to registrants before the workshop.)

Abstract: Scientific Machine Learning (SciML) is revolutionizing how we model complex physical systems by blending classical numerical analysis with the flexibility of deep learning. This workshop offers a deep dive into the core principles of SciML, specifically focusing on the power of Neural Operators. Using DeepONet as our primary example, we will demonstrate how to move beyond simple function approximation toward learning the underlying operators of partial differential equations (PDEs).

Topics covered in the workshop include: 

• The SciML Landscape: An overview of deep learning fundamentals and the transition from conventional surrogate models to mesh-independent neural operators. 
• DeepONet in Practice: A practical introduction to DeepONet architectures based on multilayer perceptrons (MLPs), with demonstrations on problems such as anti-derivatives and heat conduction.
•  Advanced Architectures: Strategies for scaling SciML models using CNN-based branch networks to capture complex spatial dependencies in thermal systems. 
• Future Frontiers: A discussion of recent advances in operator learning and the growing role of SciML in modern engineering and physical sciences.

Prerequisites: No prior experience with SciML is required, though familiarity with basic machine learning concepts and Python is recommended.

Instructor Bio: Diab Abueidda is a Research Scientist at the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign. His research spans deep learning, artificial intelligence, solid mechanics, multiphysics, and additive manufacturing, with a particular focus on applying scientific machine learning to computational engineering problems. 

Qibang Liu is a Research Scientist at the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign. His research interests span scientific machine learning, AI-aided engineering, multi-physics simulation, computational mechanics, peridynamics, and advanced manufacturing.

Hands-on participation: The workshop will use Google Colab for hands-on demonstrations.

Register by April 8, 2026