Quick Links:

C3.ai DTI Webpage

Events

Information on Call for Proposals

Proposal Matchmaking (Archive from 2020 CFP)

C3.ai DTI Training Materials

C3 Administration (password protected)

Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)

Announcing our Third Call for Proposals:

AI to Transform Cybersecurity and Secure Critical Infrastructure

It is anticipated that up to USD $10 million in Research Awards will be awarded from this Call for Proposals. Proposals can request funding of USD $100,000 to $1,000,000 for an initial period of one (1) year.

C3DTI will also make available up to USD $2 million in Azure Cloud computing resources, supercomputing resources at UIUC’s NCSA and LBNL’s NERSC, and free, unlimited access to the C3 AI Suite hosted on the Microsoft Azure Cloud. 

**** We will be offering a virtual session with information on the CFP including an opportunity to ask questions early in January. Please keep an eye out on you email and this wiki page for more information. ****

Proposals are Due February 7, 2022

Awards will be made in March 2022 with start dates of around June 1, 2022

Announcements

Colloquium on Digital Transformation Science

October 28, 3 pm CT

Deep Learning to Replace, Improve, or Aid CFD analysis in Built Environment Applications

Wei Liu, Assistant Professor of Civil and Architectural Engineering, KTH Royal Institute of Technology

REGISTER FOR ZOOM WEBINAR

Fast and accurate airflow simulations in the built environment are critical to provide acceptable thermal comfort and air quality to occupants. Computational Fluid Dynamics (CFD) offers detailed analysis on airflow motion, heat transfer, and contaminant transport in indoor environments, as well as wind flow and pollution dispersion around buildings in urban environments. However, CFD still faces many challenges, mainly in terms of computational expense and accuracy. With the increasing availability of large amounts of data, data-driven models are starting to be investigated to either replace, improve, or aid CFD simulations. More specifically, the abilities of deep learning and Artificial Neural Networks (ANN) as universal non-linear approximators, handling of high dimensionality fields, and lower computational expense are very appealing. In built environment research, deep learning applications to airflow simulations show the ANN as a surrogate, replacement for expensive CFD analysis. Surrogate modeling enables fast or even real-time predictions, but usually at the cost of degraded accuracy. This talk presents the deep learning interactions with fluid mechanics simulations in general and proposes different techniques other than surrogate modeling for built environment applications. There are promising methods largely yet to be explored in the built environment scene.

Quick Links:

C3.ai DTI Webpage

Events

Information on Call for Proposals

Proposal Matchmaking

C3.ai DTI Training Materials

C3 Administration (password protected)

Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)