Fall 2026

Transport Phenomena in Materials

MS&E 331 introduces transport phenomena principles and their application to materials design and processing.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

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Recent recorded grades — Spring 2024: 3.00 GPA, 40.0% A/AB (n=25 letter grades); Spring 2025: 3.08 GPA, 41.7% A/AB (n=24 letter grades); Spring 2026: 3.00 GPA, 27.8% A/AB (n=18 letter grades).

Grade history

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average GPA
letter grades
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Prerequisites

Course map

M S & E 330 and (MATH 319, 320, 376, or concurrent enrollment), or member of Engineering Guest Students

MS&E 331 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

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Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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Meeting source records

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Student experience

the class

Recent recorded grades — Spring 2024: 3.00 GPA, 40.0% A/AB (n=25 letter grades); Spring 2025: 3.08 GPA, 41.7% A/AB (n=24 letter grades); Spring 2026: 3.00 GPA, 27.8% A/AB (n=18 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Fluid flow, heat transfer, and diffusion
  • Materials design and processing

Skills

  • Understanding basic principles of fluid flow, heat transfer, and diffusion.
  • Applying transport principles to materials design and processing.

Grades

Latest available · Spring 2026— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

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Grade history

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "5d5ec998e0520de1bfdd3a9b6782580769a15b44840e1990c36d9ae41c792c9d",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}