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Sep 04, 2024
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PHYS 480 - Advanced Laboratory III: Modeling, Design, and Analysis Units: 3 An intensive laboratory course focused on the modeling, design, construction, and analysis of experiments. Discussion Units: 1; Lab Units: 2.
Strongly Recommended Preparation: PHYS 230. Prerequisites: PHYS 137 or PHYS 126. Possible Instructional Methods: On-ground. Grading: A-F or CR/NC (student choice). Course Typically Offered: Variable Intermittently
Student Learning Outcomes - Upon successful completion of this course students will be able to:
- Modeling : students will be able to (a) quantitatively model the physical system under investigation; (b) model the measurement system and understand issues associated with precision and accuracy of the equipment; (c) use appropriate statistical analysis methods to quantitatively compare experimental results to the physical model; (d) articulate the limitations of quantitative models; (e) appropriately revise, adjust, and generalize their models based on experimentation.
- Design : students will be able to design, construct, and troubleshoot experimental apparatuses, as well as test, calibrate, and re-design where appropriate.
- Communication : students will be able to (a) effectively argue in favor of their conclusions from their experimentation, calculations, and analysis using appropriate methods of discourse adopted by the professional physics community; (b) keep a clear and comprehensive record of their experimental work in a laboratory notebook; (c) present a well-organized, quantitative argument in the form of a written report; (d) present a clear, well-organized oral argument concerning their experiments to their peers.
- Technical skills : students will be able to (a) use both basic and advanced test and measurement equipment (such as multimeters, oscilloscopes, lock-in amplifiers, spectrum analyzers, ADC and DAC electronics); (b) design, construct, and troubleshoot experimental equipment; (c) write basic Labview/Python programs for signal processing and feedback control in experiments; (d) use computational packages (such as Mathematica/MatLAB/Python) to make computational models and predictions as well as perform statistical analysis of data; (e) use appropriate tools for the construction of experimental apparatuses such as soldering of electronic components, machining, milling, 3D printing.
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