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CoDEx 2026 Sponsor Presentations

Thank you to our CoDEx 2026 corporate sponsors for providing the following presentations, which will be held in the Arch Room.

 

Comsol logo2:05 to 2:25 p.m.  - Comsol

Accelerating Simulation Apps with Data - Driven Surrogate Models

Akhilesh Sasankan, COMSOL, Inc. 

High-fidelity simulations are essential for accuracy but can be computationally expensive, often limiting real-time analysis and rapid design iteration. This session will demonstrate how to overcome these limitations by using data-driven surrogate models, such as deep neural networks (DNN), to create fast, lightweight approximations of full finite element models.

We will discuss the workflow in the COMSOL Multiphysics® software for generating training data using efficient geometry sampling, as well as training these surrogates to achieve near-instantaneous results. Through live demonstrations of simulation apps, you can get a better idea of how surrogate models enable seamless user experiences and wider adoption of simulation across organizations.

Google Cloud logo3:05 to 3:25 p.m. - Google Cloud

Scientific Literature Assistants: Embedding Spaces and Retrieval Augmented Generation for AI Applications

John Cecala - Google Higher Education Advocate | Research & AI | Student Success

Ask any researcher, and they'll tell you that great scientific work is measured in years, often a decade or more. But AI has permanently changed that timeline. We’re at a transformational moment where AI is now an indispensable partner to scientific discovery. The technology, methods, and data Google uses to develop novel ideas for solving some of the world's greatest challenges are now available to you at Northwestern University.
  • For half a century, one of the grand challenges in medicine was predicting how proteins fold into their complex 3D shapes. A problem so difficult it was thought to be unsolvable. Google AI solved it.
  • In Materials Science, discovering a new stable material, such as those used in batteries or solar panels, can take decades. Google AI analyzed over 380K hypothetical materials, identifying over 1,000 new stable compounds, effectively condensing 800 years of research into a matter of weeks.
  • Some mathematical algorithms used in computer science have been the same for over 50 years. Google AI discovered a more efficient way to perform matrix multiplication that no human mathematician had identified.
Come learn more in this session from John Cecala - Google Higher Education Advocate | Research & AI | Student Success