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Snapshot · OMB 2025 inventory · processed Oct 11, 2026
DOE · DOE-517 · record R3375

Develop a Machine Learning Framework for Optimal Computational Campaigns for Complex Uncertain Systems

Department of Energy · BNL - Brookhaven National Laboratory (SC43 OIM)

Overview

Development stage
In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation
Not high-impactSource: c) Not high-impact
Impact justification
Does not meet definition
Start date
Not reported
Withheld from public reporting
NoSource: a) No

Mission

Topic area
Science
Operational functions
No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

'Two projects to use machine learning to acclerate the design of optimal strategies and robust computational campaigns for complex systems in the presence of substantial data and model uncertainty, and/or which have processes that span multiple scale

Expected benefits

'Two projects to use machine learning to acclerate the design of optimal strategies and robust computational campaigns for complex systems in the presence of substantial data and model uncertainty, and/or which have processes that span multiple scales'

System outputs

'Two projects to use machine learning to acclerate the design of optimal strategies and robust computational campaigns for complex systems in the presence of substantial data and model uncertainty, and/or which have processes that span multiple scales'

Technology

AI classification
Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s)
Not reported
Custom-developed code
Not reported
Public source code
Not reported

Sourcing

How it was built
Not reported
Vendor (as reported)
Not reported
Vendors (standardized)
None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII
Not reported
Privacy Impact Assessment
Not reported
Authorization to Operate
Not reported
Demographic features
Not reported
Training and evaluation data
Not reported
Federal Data Catalog
Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported
AI impact assessmentNo answer reported
Independent reviewNo answer reported
Ongoing monitoringNo answer reported
Operator trainingNo answer reported
Fail-safeNo answer reported
Appeal processNo answer reported
User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported