ONHS Report: HQ003427AOI5

Area of Interest: OSW Cost Assessment and Program Evaluation (CAPE) Modeling and Simulation

Solicitation ID: HQ003427AOI5

Agency: Department of War/CAPE

Type: Combined Synopsis/Solicitation

Deadline: 2026-11-12T16:00:00 ET

Eligibility: Maybe (Topic Dependent)

Funding: N/A

Doc Type: BAA

Clearance: No

Program Manager: danny.j.uhr.civ@mail.mil

Last Updated: 2026-11-02 17:03

Analyzed File: P07 Phase I HQ003427AOI05 OSW CAPE Modeling and Simulation.docx

Eligibility Reasoning

Requirements for participation include being able to deliver the technical attributes and capabilities described in the Area of Interest along with satisfying at least one of the conditions for eligibility outlined in the CSO HQ0034259WHSCSO Section I.e. Eligibility.

Note: This is an AI-generated summary...

Summary

The Department of War's Cost Assessment and Program Evaluation (CAPE) seeks a commercial, turnkey, managed high-performance computing solution to replace its slow and inflexible legacy Modeling & Simulation (M&S) infrastructure. The objective is to dramatically accelerate analytical support for defense investment decisions by establishing a next-generation, three-layer architecture: a high-performance simulation execution layer, a machine-learned surrogate emulation layer, and a self-service quantitative analysis layer. Key technical requirements include the capacity to run up to 150,000 simulations in under two weeks (a task currently requiring one year), operation at Impact Level 6 (IL6), support for a diverse software ecosystem including Python, R, and AI/ML tools, and network throughput of 100 Mbps-1 Gbps. The commercial provider will be responsible for the underlying platform and infrastructure IT operations, while the government will maintain full administrative control over the analytical environment. (Source: P07 Phase I HQ003427AOI05 OSW CAPE Modeling and Simulation.docx.)

Overall Technical Areas

Campaign-level Modeling & SimulationHigh-Performance Computing (HPC) ArchitecturePhysics-based Simulation EnginesMachine Learning Surrogate ModelsNeural Network DevelopmentDesign of Experiments (DoE)Quantitative Sensitivity AnalysisTradespace Decision AnalysisScalable Data PlatformsData Pipeline IntegrationContainer Orchestration & ParallelizationArtificial Intelligence (AI) / LLMsNumerical Optimization Tools

Probable LBNL Areas

BSACSAEESAESAETA

Focus Areas

Example Priorities:

A high-performance simulation execution layer to execute large numbers of model (e.g., AFSIM) runs to span the parameter space.
Execute computationally intensive and resource demanding runs infrequently and systematically using advanced Design of Experiments techniques.
Proposed solution architectures should span options to allow for parameter evaluation in the simulation layer on the order of 1,200 individual simulations... to be executed within 10 hours real-time, up to 150,000 individual simulations... to be executed within 2 weeks real-time.

Specific Technical Skills:

High-Performance Computing (HPC)Parallel ProcessingSimulation ModelingAFSIMDesign of Experiments (DoE)Campaign AnalyticsBatch ProcessingResource SchedulingComputational Physics

Potential PIs for this Area:

Name	Score	Organization
Scott Baden	0.77	CSA \| AMCR \| Computer Science · Parallel Processing
Erich Strohmaier	0.77	CSA \| AMCR \| Computer Science · High-Performance Computing (HPC)
Suren Byna	0.76	CSA \| SciData \| Data Science Research · High-Performance Computing (HPC)
Angelos Ioannou	0.76	CSA \| AMCR \| Computer Science · Parallel Processing
Reijo Keskitalo	0.75	CSA \| SciData \| Data & Computational Science · Computational Physics
Houjun Tang	0.75	CSA \| SciData \| Data Science Applications · Batch Processing
Zachary Needell	0.74	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Simulation Modeling
Kunhwi Kim	0.74	EESA \| EG \| Geophysics Dept · AFSIM
Rich Wolski	0.73	CSA \| ESNET \| Science Engagement · Resource Scheduling
Zixi Hu	0.73	CSA \| AMCR \| Mathematics · Design of Experiments (DoE)

Example Priorities:

A machine-learned surrogate emulation layer to train ultra-fast emulator models from simulation results and detect out-of-sample queries.
Provide the extensible data staging, pipeline integration, and execution framework to ingest batch simulation results and host these government-developed models.
Ability to accommodate... artificial intelligence capabilities (e.g., large language models), and neural network development (e.g., PyTorch)...

Specific Technical Skills:

Machine LearningSurrogate ModelingNeural NetworksPyTorchData EngineeringETL PipelinesModel TrainingAnomaly DetectionData Staging

Potential PIs for this Area:

Name	Score	Organization
Gazi Mahmud	0.75	BSA \| Env. Genomics & Sys. Biology \| Molecular Ecosystems Bio · Data Engineering
Wei Zhang	0.75	CSA \| SciData \| Data Science Research · Data Engineering
Talita Perciano Costa Leite	0.74	CSA \| SciData \| Data & Computational Science · Machine Learning
Devarshi Ghoshal	0.74	CSA \| SciData \| Data Science Applications · Data Staging
Christoph Kirst	0.74	CSA \| SciData \| Data & Computational Science · Neural Networks
Nicholas Tyler	0.73	CSA \| NERSC \| Sci Eng & Workflows Department · ETL Pipelines
Ling Jin	0.72	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Model Training
Xiaoya Chong	0.72	ESA \| ALS \| PS Computing staff · PyTorch
Matthew Zahr	0.72	CSA \| AMCR \| Mathematics · Surrogate Modeling
Mah Kadidia Konate	0.71	CSA \| NERSC \| Sci Eng & Workflows Department · Anomaly Detection

Example Priorities:

An intuitive self-service quantitative analysis layer housing pre-built methods for sensitivity analysis, marginal gain estimates, etc.
Tools for custom analysis (e.g., tradespace decisions between range, speed, and lethality of weapon systems) and visualization.
Provide leadership in developing and promoting improved analytical tools and methods for analyzing national security planning and the allocation of resources.

Specific Technical Skills:

Data VisualizationQuantitative AnalysisSensitivity AnalysisTradespace AnalysisDecision Support SystemsStatistical ModelingUser Interface DesignSelf-Service Analytics

Potential PIs for this Area:

Name	Score	Organization
Sarah Poon	0.75	CSA \| SciData \| Data Science Applications · User Interface Design
Likun Zhang	0.75	EESA \| CESD \| Earth System Sciences · Statistical Modeling
Zixi Hu	0.74	CSA \| AMCR \| Mathematics · Quantitative Analysis
Rajshree Deshmukh	0.74	CSA \| SciData \| Data Science Applications · User Interface Design
Eric Masanet	0.73	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Tradespace Analysis
Thomas Hendrickson	0.73	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Tradespace Analysis
Annette Greiner	0.73	CSA \| NERSC \| Sci Eng & Workflows Department · Data Visualization
Zachary Needell	0.72	ETA \| Energy Analysis \| Systems & Energy Tech Analysis · Decision Support Systems
Milica Grahovac	0.72	ETA \| Energy Analysis \| Energy Efficiency Studies · Sensitivity Analysis
Anna Giannakou	0.72	CSA \| SciData \| Data Science Applications · Self-Service Analytics

Example Priorities:

A high-performance compute environment sized to meet demanding modeling, simulation, and analytic workloads.
The Commercial provider will be responsible for turnkey, managed platform and infrastructure IT operations, including hardware availability, operating system patching, network performance, automated system backups, and baseline cybersecurity hardening.
The virtual infrastructure and HPC architecture must operate at Impact Level 6 (IL6).
Ability to accommodate evolving development ecosystems and diverse sets of software including... modern software (e.g., Python, R, Java, etc.), scalable data platforms, scalable parallelizable processes (e.g., containers)...

Specific Technical Skills:

Cloud Architecture (IL6)Managed IT ServicesCybersecurity HardeningContainerizationDevSecOpsNetwork EngineeringAPI IntegrationSystem AdministrationPython/R/Java Environments

Potential PIs for this Area:

Name	Score	Organization
Arpit Gupta	0.76	CSA \| ESNET \| Tech Adv and Engagement · Network Engineering
Tiffany Connors	0.75	CSA \| NERSC \| Data Center Department · Cybersecurity Hardening
Cory Snavely	0.75	CSA \| NERSC \| HPC Technology Department · System Administration
Dipak Ghosal	0.75	CSA \| ESNET \| Tech Adv and Engagement · Network Engineering
Cees De Laat	0.74	CSA \| ESNET \| Tech Adv and Engagement · Cloud Architecture (IL6)
Stefan Lasiewski	0.74	CSA \| NERSC \| HPC Technology Department · Containerization
Gregory Lemieux	0.74	EESA \| CESD \| Earth System Sciences · Python/R/Java Environments
Abdelilah Essiari	0.74	CSA \| SciData \| Data Science Applications · Cybersecurity Hardening
Chris Harris	0.73	CSA \| NERSC \| Sci Eng & Workflows Department · Python/R/Java Environments
Seth Carbon	0.72	BSA \| Env. Genomics & Sys. Biology \| BioSystems Data Science · API Integration

Potential LBNL PIs (Overall)

Note on PI Matching: These suggestions are generated through an AI-driven semantic analysis of LBNL staff profiles.

Name	Score	Organization
Angelos Ioannou	0.78	CSA \| AMCR \| Computer Science · High-Performance Computing (HPC) Architecture
Christoph Kirst	0.76	CSA \| SciData \| Data & Computational Science · Neural Network Development
Stefan M. Wild	0.76	CSA \| Computing · Numerical Optimization Tools
Cees De Laat	0.76	CSA \| ESNET \| Tech Adv and Engagement · Scalable Data Platforms
Eric Chagnon	0.75	CSA \| AMCR \| Mathematics · Artificial Intelligence (AI) / LLMs
Nicholas Tyler	0.75	CSA \| NERSC \| Sci Eng & Workflows Department · Data Pipeline Integration
Jingjing Zhang	0.73	ETA \| Building & Industrial Energy Systems \| Whole Building Sys Dept · Tradespace Decision Analysis
Zhe Bai	0.73	CSA \| AMCR \| Computer Science · Machine Learning Surrogate Models
Oluwamayowa Amusat	0.73	CSA \| SciData \| Data Science Applications · Design of Experiments (DoE)
Kunhwi Kim	0.73	EESA \| EG \| Geophysics Dept · Physics-based Simulation Engines

Fundamental Research Exemption (FRE)

Not Mentioned