TAM 598

Spring 2025 All Classes

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Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in theoretical and applied mechanics intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

May be repeated in the same or separate terms if topics vary to a maximum of 12 hours.

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TAM 598 class schedule data for spring 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
60823	Lecture-Discussion	EN1	9:00AM -10:50AM	MW	2045 Sidney Lu Mech Engr Bldg	Ertekin, E	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Eng & Sci App of Deep Learning Section Info: Prerequisites: Math 257 Linear Algebra with Computational Applications (or equivalent); CS 12 Intro to Computer Science. This class explores the application of deep learning techniques to solve complex problems in engineering and the physical sciences. Students will learn to implement and adapt neural networks, convolutional architectures, and recurrent models to analyze data and discover patterns in physical systems. Topics include data-driven modeling, inverse design, generative approaches for applications in mechanics and materials, and integration with domain-specific physics constraints. The course emphasizes practical implementation, with hands-on projects that involve tasks such as simulation optimization, parameter estimation, and prediction in engineering systems. Restriction(s): Restricted to Graduate - Urbana-Champaign.
62823	Online Lecture	ENC	ARRANGED	n.a.	n.a.	Ertekin, E	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Eng & Sci App of Deep Learning Section Info: Prerequisites: Math 257 Linear Algebra with Computational Applications (or equivalent); CS 12 Intro to Computer Science. This class explores the application of deep learning techniques to solve complex problems in engineering and the physical sciences. Students will learn to implement and adapt neural networks, convolutional architectures, and recurrent models to analyze data and discover patterns in physical systems. Topics include data-driven modeling, inverse design, generative approaches for applications in mechanics and materials, and integration with domain-specific physics constraints. The course emphasizes practical implementation, with hands-on projects that involve tasks such as simulation optimization, parameter estimation, and prediction in engineering systems. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MS:Mechanical Engineerng -UIUC, PHD:Mechanical Enginerng -UIUC, MS:Theor&Appl Mechanics -UIUC, or PHD:Theor&Appl Mechanics -UIUC.
62822	Online	EON	ARRANGED	n.a.	n.a.	Ertekin, E	Part of Term: 1 Date Range: 01/21/25-05/07/25 Credit: 4 hours Section Title: Eng & Sci App of Deep Learning Section Info: Prerequisites: Math 257 Linear Algebra with Computational Applications (or equivalent); CS 12 Intro to Computer Science. This class explores the application of deep learning techniques to solve complex problems in engineering and the physical sciences. Students will learn to implement and adapt neural networks, convolutional architectures, and recurrent models to analyze data and discover patterns in physical systems. Topics include data-driven modeling, inverse design, generative approaches for applications in mechanics and materials, and integration with domain-specific physics constraints. The course emphasizes practical implementation, with hands-on projects that involve tasks such as simulation optimization, parameter estimation, and prediction in engineering systems. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MS:Mechanical Engineerng -UIUC, MS: Civil Engr - Online - UIUC, MS:Industrial Engr Online-UIUC, MS:Env Engr CivilEngr ONL-UIUC, NDEG:Engineering GR ONL - UIUC, MS: Aerospace Engr-Online-UIUC, MENG:Mech Engineering Onl-UIUC, MENG:Elec & Comp Eng ONL -UIUC, MENG:Engr:Energy Sys Onl-UIUC, MENG:Engr:AeroSys Online- UIUC, or MENG:ENGR:Digital Ag ONL- UIUC.