Spring 2027

Methods for Probabilistic Risk Analysis of Nuclear Power Plants

Covers probabilistic risk analysis for nuclear power plants using fault trees, event trees, Bayesian data analysis, and probabilistic risk management.

offering recorded3 credits
Recorded instructors · Spring 2027 Juliana Pacheco Duarte

Summary

1 / 3

Recent recorded grades — Spring 2016: 3.56 GPA, 75.0% A/AB (n=16 letter grades); Spring 2018: 3.29 GPA, 58.3% A/AB (n=12 letter grades); Spring 2025: 3.85 GPA, 90.0% A/AB (n=10 letter grades).

What you’ll be able to do

  • Correctly apply methods of fault tree, event-tree, data, and uncertainty analysis to evaluate potential risks of engineering systems

  • Recognize, formulate, and analyze risks of engineered systems

  • Explain the results of risk analysis to managers and other non-specialist decision-makers

Source: UW Guide 1 UW Guide 2

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(STAT/​MATH 309,STAT 311, 324, or STAT/​MATH 431), graduate/professional standing, or member of Engineering Guest Students

ISYE/NE 574

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Historical instructors & teaching patterns

JULIANA PACHECO DUARTE is recorded teaching in Spring 2023, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 200
LEC 001Classroom Instruction0 / 200

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2016: 3.56 GPA, 75.0% A/AB (n=16 letter grades); Spring 2018: 3.29 GPA, 58.3% A/AB (n=12 letter grades); Spring 2025: 3.85 GPA, 90.0% A/AB (n=10 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Fault trees, event trees, Bayesian data analysis, and probabilistic risk management
  • Risk and reliability analysis of engineered systems in the nuclear power industry

Skills

  • Apply fault tree, event-tree, data, and uncertainty analysis methods
  • Recognize, formulate, and analyze risks of engineered systems
  • Explain risk analysis results to non-specialists

Grades

Latest available · Spring 2025— not enough history to project Spring 2027.

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 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
ISYE/NE 574 · Spring 2027

Methods for Probabilistic Risk Analysis of Nuclear Power Plants

Recorded 2026-10-14
ISYE/NE 574 · Spring 2027

Methods for Probabilistic Risk Analysis of Nuclear Power Plants

Recorded 2026-10-14
Raw records 
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    "course_id": "ISYE/NE 574",
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    "source_subject_id": "490",
    "title": "Methods for Probabilistic Risk Analysis of Nuclear Power Plants",
    "credits_min": 3,
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    "offering_id": "1274:684:013835",
    "course_id": "ISYE/NE 574",
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    "term_id": "1274",
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    "title": "Methods for Probabilistic Risk Analysis of Nuclear Power Plants",
    "credits_min": 3,
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "e64a6ac3fb14770e157211b6a462fd1955ad77a4aefa5d586330ce7e6be43e1f",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}