SE 598

Fall 2021 All Classes

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Credit: 1 TO 4 hours.

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

1 to 4 graduate hours. No professional credit. May be repeated in the same or separate terms if topics vary to a maximum of 12 hours.

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SE 598 class schedule data for fall 2021
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	76141	Online Lecture	YO	3:30PM -4:50PM	TR	n.a.	Li, Y	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: ML in Materials Design Section Info: Prerequisites: MATH 241 & IE 300 In this course, we will explore the benefits of introducing machine learning into material design and how to efficiently integrate machine learning at different level of material analysis and design process for accelerating novel material discovery. The tentative contents that plan to be covered in this course include introduction to computational material simulation, data-driven experimental design, hidden structure identification, data fusion, physics-based machine learning, and adaptive sampling and surrogate modeling. Design applications in energy storage systems, low dimensional materials, polymer based composites and additive manufacturing will be used as case studies for demonstration in this course. Restriction(s): Restricted to Graduate - Urbana-Champaign.
	76140	Lecture-Discussion	YU	3:30PM -4:50PM	TR	204 Transportation Building	Li, Y	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: ML in Material Design Section Info: Prerequisites: MATH 241 & IE 300 In this course, we will explore the benefits of introducing machine learning into material design and how to efficiently integrate machine learning at different level of material analysis and design process for accelerating novel material discovery. The tentative contents that plan to be covered in this course include introduction to computational material simulation, data-driven experimental design, hidden structure identification, data fusion, physics-based machine learning, and adaptive sampling and surrogate modeling. Design applications in energy storage systems, low dimensional materials, polymer based composites and additive manufacturing will be used as case studies for demonstration in this course. Restriction(s): Restricted to Graduate - Urbana-Champaign.