ME 598

Spring 2026 Part of Term 1

Part of Term 1
Jan 20-May 6

Special Topics

SHOW FINAL EXAM SCHEDULE

Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in mechanical engineering 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.

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ME 598 class schedule data for spring 2026
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
48380	Lecture-Discussion	DM	11:00AM -12:20PM	TR	2051 Sidney Lu Mech Engr Bldg	Schleife, A	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Intro to Digital Materials Section Info: Meets with MSE 598 (70522) and CSE 498 (70543). We introduce the connection of materials and data science, and specific issues regarding experimental and computational materials data. Topics include data acquisition and management, data curation, uncertainty quantification, and applying machine learning to materials data. The focus will be on current scientific literature in the emerging materials and data area. Restriction(s): Restricted to Graduate - Urbana-Champaign.
45548	Lecture-Discussion	JWM	ARRANGED	n.a.	Location Pending	Maduzia, J	Part of Term: 1 Date Range: 01/20/26-05/06/26 Special Approval: Departmental Approval Required Credit: 4 hours Section Title: MicroNano Fabrication Capstone Section Info: This course is a hands-on micro-nano fabrication course. The student will be assigned a process topic including but not limited to spinning, exposure, PVD, RIE or ICP DRIE etching. Through in-lab training, the student will fabricate patterned test samples with micron scale resolution, then run a parameter study on the process. The student will then characterize the samples (SEM, profilometery, etc), analyze the results, and present the results for review. A report of the work accomplished, process used, and results will be provided for review. Students enrolled in this course will have a unique opportunity to fabricate and characterize micro-nano samples using modern techniques and equipment. Pre-requisites: Previously taken or concurrently enrolled in ME487 OR ME 586 OR ME 588 OR ECE444. Restriction(s): Restricted to MENG:Mechanical Enginerng-UIUC.
55825	Lecture-Discussion	NH	3:30PM -4:50PM	TR	403B2 Engineering Hall	Gahlawat, A	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Dist Robust Cntrl&Optimization Section Info: Prerequisites Linear Systems Theory • Probability and Random Processes • Introductory Control Theory (feedback design, Lyapunov methods) • Familiarity with convex optimization and basic machine learning helpful but not required. This course introduces methods for modeling, analysis, and control of uncertain continuous time systems with emphasis on distributional robustness. It begins with stochastic processes and stochastic differential equations, including probability theory, Brownian motion and Itˆo calculus, and pathwise and distributional representations. Stability of uncertain stochastic systems is studied using Lyapunov methods and generalized robustness concepts defined through metrics on probability measures. The course then covers adaptive control design, starting with deterministic systems and finite-time robustness guarantees, and extending to robust adaptive control in the space of probability measures. Topics include controller design for stochastic systems, comparison with deterministic results, and separation between implementation and certificates. The final part focuses on application examples drawn from the intersection of machine learning and robotics, including distributionally robust optimization for model predictive control (MPC), safety-critical control of systems such as quadrotors and unicycles, and frameworks for certifiable integration of high-dimensional sensing and deep vision models in control. Prerequisites Linear Systems Theory • Probability and Random Processes • Introductory Control Theory (feedback design, Lyapunov methods) • Familiarity with convex optimization and basic machine learning helpful but not required Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MS:Electrical Engineerng -UIUC, MS:Industrial Engineerng -UIUC, MS:Mechanical Engineerng -UIUC, PHD:Mechanical Enginerng -UIUC, MS:Theor&Appl Mechanics -UIUC, PHD:Theor&Appl Mechanics -UIUC, MS: Aerospace Engr -UIUC, PHD: Aerospace Engr -UIUC, MENG:Mechanical Enginerng-UIUC, MENG:Elec & Computer Eng-UIUC, or MENG:Engr:AutonomyRobotic-UIUC.
74804	Online	ONL	ARRANGED	n.a.	n.a.	Gahlawat, A	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Dist Robust Cntrl&Optimization Section Info: Prerequisites Linear Systems Theory • Probability and Random Processes • Introductory Control Theory (feedback design, Lyapunov methods) • Familiarity with convex optimization and basic machine learning helpful but not required. This course introduces methods for modeling, analysis, and control of uncertain continuous time systems with emphasis on distributional robustness. It begins with stochastic processes and stochastic differential equations, including probability theory, Brownian motion and Itˆo calculus, and pathwise and distributional representations. Stability of uncertain stochastic systems is studied using Lyapunov methods and generalized robustness concepts defined through metrics on probability measures. The course then covers adaptive control design, starting with deterministic systems and finite-time robustness guarantees, and extending to robust adaptive control in the space of probability measures. Topics include controller design for stochastic systems, comparison with deterministic results, and separation between implementation and certificates. The final part focuses on application examples drawn from the intersection of machine learning and robotics, including distributionally robust optimization for model predictive control (MPC), safety-critical control of systems such as quadrotors and unicycles, and frameworks for certifiable integration of high-dimensional sensing and deep vision models in control. Restriction(s): Restricted to MS: Civil Engr - Online - UIUC, MS:Industrial Engr Online-UIUC, MS:Mechanical Engineerng -UIUC, MS:Env Engr CivilEngr ONL-UIUC, NDEG:Engineering GR ONL - UIUC, MS: Aerospace Engr-Online-UIUC, MENG:Mech Engineering Onl-UIUC, or MENG:Elec & Comp Eng ONL -UIUC.
68059	Lecture-Discussion	SML	9:30AM -10:45AM	TR	2036 Campus Instructional Facility	West, M	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Scientific Machine Learning Section Info: Theory and practice of Scientific Machine Learning (SciML), which leverages machine learning tools for scientific computing. Topics include learning-based methods for differential equations, neural ODEs and PDEs, physics-informed networks and model discovery, interpretable and explainable learning, differentiable and probabilistic programming for scientific computing, and uncertainty quantification via learning. Efficient parallel implementation of algorithms on scalable computing architectures will be emphasized. Requires familiarity with introductory numerical methods (e.g., TAM 470 or CS 357) and the basics of machine learning and neural networks (e.g., CS 446). Restriction(s): Restricted to Graduate - Urbana-Champaign.
37500	Lecture	ST	3:00PM -4:20PM	MW	3100 Sidney Lu Mech Engr Bldg	Tawfick, S	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Additive Mfg of Polymers Section Info: This course will cover Additive Manufacturing of Polymeric Materials and 3D printing processes. The topics include polymerization, photo curing and frontal polymerization-based additive manufacturing of thermosetting polymers. It will also cover fluid deposition modeling of thermoplastic polymeric materials. The course will fundamental fundamental aspects of these materials, processes and transport phenomena. Restriction(s): Restricted to Graduate - Urbana-Champaign.
69408	Lecture	SX	12:30PM -1:50PM	MW	2051 Sidney Lu Mech Engr Bldg	Xu, S	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Soft Robotics Fundamentals Section Info: The field of soft robotics has progressed rapidly in recent years, offering innovative alternatives to conventional rigid robotic systems. This course will explore emerging paradigms in soft robotics, covering topics of design, fabrication, materials, mechanics, control methods, and applications. Students will develop a foundational understanding of commonly used sensing (e.g., electrical, optical, magnetic) and actuation (e.g., electrical, pneumatic, thermal) mechanisms, along with material selection and design principles grounded in classical mechanics. Applications discussed will include bio-inspired robots, wearable systems, surgical devices, implantable actuators, etc. Restriction(s): Restricted to Graduate - Urbana-Champaign.
74800	Lecture-Discussion	WZ1	3:00PM -4:50PM	MW	410C1 Engineering Hall	Zhang, L	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Applied Heat Transfer Section Info: Review of fundamentals of convective heat transfer, boiling and condensation with industrial applications, heat transfer principles for heat exchanger design and/or performance evaluations, convective heat transfer in porous media with industrial applications and numerical methods in convective heat transfer. Restriction(s): Restricted to Graduate - Urbana-Champaign.
74799	Online	WZC	ARRANGED	n.a.	n.a.	Zhang, L	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Applied Heat Transfer Section Info: Section reserved for students designated as On Campus. Review of fundamentals of convective heat transfer, boiling and condensation with industrial applications, heat transfer principles for heat exchanger design and/or performance evaluations, convective heat transfer in porous media with industrial applications and numerical methods in convective heat transfer. Restriction(s): Restricted to MS:Computer Science -UIUC, PHD:Computer Science -UIUC, MS:Electrical Engineerng -UIUC, MS:Industrial Engineerng -UIUC, PHD:Industrial Enginerng -UIUC, MS:Mechanical Engineerng -UIUC, PHD:Mechanical Enginerng -UIUC, MENG:Mechanical Enginerng-UIUC, or MENG:Elec & Computer Eng-UIUC.
74794	Online	WZO	ARRANGED	n.a.	n.a.	Zhang, L	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Applied Heat Transfer Section Info: Section reserved for Online students. Review of fundamentals of convective heat transfer, boiling and condensation with industrial applications, heat transfer principles for heat exchanger design and/or performance evaluations, convective heat transfer in porous media with industrial applications and numerical methods in convective heat transfer. Restriction(s): Restricted to MS:Industrial Engr Online-UIUC, MS:Mechanical Engineerng -UIUC, MENG:Mech Engineering Onl-UIUC, or MENG:Elec & Comp Eng ONL -UIUC.