United States

A system for the real-time estimation of an evolving underwater plume using a single sensor is presented. This system includes the plant, which models the evolving plume and provides synthetic data, and the estimator, which detects, measures, and reconstructs the plume using a hybrid Luenberger-naive observer. Preliminary results are shown, and current work in the measurement of gradients and trajectory planning are discussed.

NAML 2025

Real-Time Plume Estimation with an Autonomous Underwater Vehicle

estimation

computational fluid dynamics

optimization

### Welcome to the 10th annual workshop on Naval Applications of Machine Learning!

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NAML 2026

The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



poster

### Welcome to the 9th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. 
 Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications
* To connect machine learning researchers with experts in military requirements
* To build and strengthen collaborations within the defense research community

#### *"What we do as a Navy is a human business. Don’t pass up an opportunity to come together as a team. Don’t pass up leadership opportunities, and don’t outsource them to a PowerPoint. If you have an opportunity to come together as a team, to increase belonging, do it."*
Admiral John M. Richardson, 31st Chief of Naval Operations

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The 9th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.

Data unification is the process of combining data from multiple sources into a single, consistent view. It helps resolve inconsistencies, redundancies, and discrepancies in the data. This creates benefits for many business operations such AI, Analytics, and Cyber Security analysis. Data unification also promotes data governance and trustworthy AI. Data Unification can also help Navy get around vendor lock. Data unification doesn't mean that Navy must move data. Many time it is often better to leave data where it is. Instead, we can utilize data unification tools to work with data where it currently resides.  This poster session will provide attendees with an overview of data unification tools, architecture, and processes beneficial to helping Navy achieve advance analysis, AI, and Cyber Security goals.

Optimizing AI with a Unified Data Layer

NAUTICAL will establish a workforce development strategy, a competency development model, and a series of courses to build upon the professional cadre scientists, engineers, and technicians able to develop and advance artificial intelligence (AI) at NIWC Pacific and other warfare. The demand for an effective, capable AI workforce is greater than ever and will continue to grow because the world appears to be at the cusp of an AI revolution. This project will focus on providing students with basic AI algorithmic theory and more practical AI implementation skills from a DoD perspective while limiting the mathematical aspects of AI.

NAUTICAL will provide different sessions for basic, intermediate and advanced levels of AI coursework, developed primarily by NIWC AI SMEs for the Navy and DoD. Basic AI for DoD 101 training will be conducted in multiple modules over the course of one to two days consisting of a series of lessons and programming assignments to cover various fundamental topics in the world of AI. Intermediate and advanced focus courses in specific stand-alone AI technology area will be provided in modules following the novice level lectures/exercises. Courses will be held virtually via Flankspeed Teams to provide flexibility and recordability for posterity. 

NAUTICAL will publicize our workforce development effort throughout NIWC Pacific and other Naval warfare centers. All DoD military, civilian, and contractors are welcome to partake in NAUTICAL. 

Guest lecturers will be recruited to provide lectures in a variety of AI topics that our local AI SME’s may not be knowledgeable about. 

Classes are planned to be structured and based off of the textbook Artificial Intelligence: A modern Approach by Stuart Russell and Peter Norvig. This textbook is nicknamed the “Bible for AI” by Doug Lange (NIWC PAC SSTM AI) and has been adopted by over 1500 universities/colleges for their AI curriculum. We plan to split the topics into 10 modules that can each be delivered in one hour. 
Novice-level topics planned to be covered include:
• Supervised/Unsupervised Learning
• Deep learning
• Computer vision
• Natural Language Processing (NLP)
• Reinforcement Learning, Q-Learning
• Multi-Agent Reinforcement Learning (MARL)
• Automated Planning/PDDL (Planning Domain Definition Language)
• Adversarial Planning, MCTS (Monte Carlo Tree Search), AlphaZero
• Probabilistic Reasoning
• Robotics
• DoD HPCMP, NavWAR HPCC, LLM AI Chat – Taught by George Istambouli and his team
• Explainable AI
• AI Dangers/Ethics
Whenever possible, following each of the lecture modules will be a Google Colab laboratory exercise for students to work on directly with help from the instructor and NAUTICAL team. Lecture modules are planned to be executed on a bi-monthly basis. 
Our plan for a potential capstone project by NAUTICAL students is for students to help them pursue labor funding for any AI/ML project ideas they are interested either:
• Propose to their existing projects, ideas for how to implement AI/ML to improve their project efficiency, cost, execution, automation, etc. 
• Formulate a NISE proposal for AI/ML, potentially using existing environments or data sources from their own projects or from various projects at NIWC Pacific or elsewhere, such as LVC, DigiCAT, AFSIM/NGTS/etc, CASREP data, AISC, etc.
• Formulate an AI/ML proposal to ONR and other potential external sponsors.

Our NAUTICAL team has extensive experience formulating and proposing successfully accepted/funded NISE proposals in AI/ML and will provide AI project incubation services to our NAUTICAL students


NAUTICAL wants YOU to learn AI!

The Navy's Cyber Security Technical Authority is using LLMs to generate cybersecurity Standards. The implementation of LLMs has fundamentally disrupted the Navy's process for developing cybersecurity standards. Our efforts have transformed a multi-year, multi-million dollar single purpose task into a reusable agile process that realizes efficiencies to cost, schedule, and performance with improved operational security and increased fidelity of the resulting products. 


Transforming Naval Cybersecurity Standards Development: The role of Scripting and Large Language Models to deliver standards tailored towards warfighter needs. 

Mission planning is a complex and dynamic process, requiring robust systems and procedures to ensure the successful planning and execution of missions. Recent developments in generative AI have the potential to revolutionize mission planning, introducing new capabilities that enhance the speed and quality of mission plans. One such capability is the generation of aerial refueling schedules with the assistance of a Large Language Model (LLM).
On their own, LLMs struggle to reliably generate any type of plan due to the complexity of the problem [1]. Often, there are many implicit and explicit constraints that must be adhered to when creating aerial refueling schedules, that the LLM may not follow. This could lead to plans that look correct at a first glance but have errors in them upon deeper inspection. There are two primary contributing factors to this problem: hallucinations and a lack of reasoning skills. Hallucinations occur due to the probabilistic nature of LLMs, since they are not grounded in truth, and rely on their training data to produce text [2]. This also means that LLMs can imitate some basic reasoning skills, but quickly struggle as soon as any complexity is added to a problem [3]. Our project exploits the strengths of LLMs while minimizing its weaknesses to create a more robust aerial refueling planning system.
Using a LLM in conjunction with an optimization algorithm, we can take a natural language description of a mission, use the LLM to translate it into a structured format, pass it into an optimization algorithm, and generate an aerial refueling schedule. This utilizes the LLMs ability to parse natural language and output it into a specialized format. The optimization algorithm is developed specifically for generating aerial refueling plans, but this could be replaced by any algorithm-based tool that requires a structured input. By using this type of framework, the LLM is now solving a much simpler problem of translating natural language into a structured format such as JSON or a Pydantic model. Additionally, structured data such as JSON or Pydantic models can easily be validated, which significantly reduces the impact of hallucinations. Our results show that this approach is successful in developing aerial refueling schedules with several types of aircraft, departing from varying locations, with several constraints regarding their fuel level. By developing this framework, we are demonstrating how to safely deploy LLMs in an operational environment.
1. Xie, Jian, et al. "Travelplanner: A benchmark for real-world planning with language agents." arXiv preprint arXiv:2402.01622 (2024).
2. Waldo, Jim, and Soline Boussard. "GPTs and Hallucination: Why do large language models hallucinate?." Queue 22.4 (2024): 19-33.
3. Yao, Shunyu, et al. "React: Synergizing reasoning and acting in language models." arXiv preprint arXiv:2210.03629 (2022).


Using Large Language Models to Assist in Air Combat Mission Planning

Propulsion and flow control design for Navy vehicle systems require rigorous analysis of the fluid dynamics around the structures. The most common experimental approach for characterizing these complex flow fields is Particle Image Velocimetry (PIV), in which image sequences are used to estimate velocity flow fields. Traditional PIV methods can be time-consuming and resource-intensive, requiring significant manual calibration and post-processing. This is a significant bottleneck in the design cycle. In this program, we develop a machine learning PIV approach to accurately estimate velocity fields from complex real-world data for existing underwater flow control and propulsion programs. This could lead to an order-of-magnitude speedup in PIV processing, accelerating from months to hours.

FLAP-PIV: Fast, Learning-based Approach to Processing for Particle Image Velocimetry

The increased utilization of Unmanned Aerial Vehicles (UAVs) in diverse missions, from humanitarian aid to combat operations, underscores the necessity for an efficient and cost-effective development workflow for autonomous systems. Especially for defense purposes, building autonomous target recognition systems capable of detecting, identifying, and classifying adversarial agents with machine learning models requires extensive data for training. Consequently, simulation software has become an essential tool for developers seeking to assess autonomous system performance and collect data across various environments. Furthermore, the transition to real-world, application-ready systems necessitates a simulation platform that replicates not only the vehicle control algorithms but also environmental factors that affect system performance, such as lighting conditions and sensor noise. 

In response to these requirements, we introduce ‘SiDG-ATRID’ (Simulator for Data Generation for Automatic Target Recognition, Identification and Detection), a simulation platform that enables the collection of high-fidelity imagery data, powered by Unreal Engine 5. The simulator supports multi-agent simulations using the AirSim API library for UAV controls and simulates commercial aircraft traffic. This framework allows for customized camera placements to record videos or photos and manage environmental conditions such as weather and lighting. Additionally, by leveraging the Cesium API for geospatial mapping, it can accurately recreate real-world environments, enhancing the realism and applicability of simulations. This integrated approach enhances the efficiency and effectiveness of synthetic data generation for training machine learning and computer vision models.


SIDG-ATRID: Simulator for Data Generation for Automatic Target Recognition, Identification and Detection

The U.S. Marine Corps (USMC) faces significant challenges in adapting its operational frameworks to embrace modern technologies, notably in the integration of advanced data sciences and AI/ML. For example, finding the root causes for risks such as mishaps or incidents regarding a warfighting system is critical for designing policies and taking actions in strategic, operational, and tactical levels to prevent future risks.
These risks may attribute to multiple factors, especially human factors such as who operates/drives an equipment. The data sources detailing the incidents, systems, and operators might reside in distributed environment, sometime they are difficult to access without necessary policies and rules to justify the needs. For example, operators’ data may include personal identifiable information (PII), demographics, and personal history. In the context of WHI, different end users may not have the rights to share data directly because of sensitive nature of human elements. Furthermore, these data sets can be structured such as age, gender, education, military occupational specialty (MOS), etc., or unstructured such as descriptions of incidents. 
In this paper, we will demonstrate a systematic methodology using advanced data sciences and AI/ML to address these challenges in the following steps:
• Provide a distributed AI/ML agents infrastructure such as collaborative learning agents (CLAs) to analyze data sources separately and then fuse the patterns and models using shared vocabularies in the patterns instead of sharing raw data.
• Apply causality analysis including counterfactual reasoning to discover root causes of risks from structured data.
• Apply and compare natural language analysis methods including lexical link analysis (LLA), AI transformers such as bidirectional encoder representations from transformers (BERT), retrieval augmented generation (RAG), knowledge graphs (KG), and large language models (LLMs) to extract themes and causes from unstructured data to characterize the risks. 
The U.S. Marine Corps (USMC) faces significant challenges in adapting its operational frameworks to embrace modern technologies, notably in the integration of advanced data sciences and AI/ML. For example, finding the root causes for risks such as mishaps or incidents regarding a warfighting system is critical for designing policies and taking actions in strategic, operational, and tactical levels to prevent future risks.
These risks may attribute to multiple factors, especially human factors such as who operates/drives an equipment. The data sources detailing the incidents, systems, and operators might reside in distributed environment, sometime they are difficult to access without necessary policies and rules to justify the needs. For example, operators’ data may include personal identifiable information (PII), demographics, and personal history. In the context of WHI, different end users may not have the rights to share data directly because of sensitive nature of human elements. Furthermore, these data sets can be structured such as age, gender, education, military occupational specialty (MOS), etc., or unstructured such as descriptions of incidents. 
In this paper, we will demonstrate a systematic methodology using advanced data sciences and AI/ML to address these challenges in the following steps:
• Provide a distributed AI/ML agents infrastructure such as collaborative learning agents (CLAs) to analyze data sources separately and then fuse the patterns and models using shared vocabularies in the patterns instead of sharing raw data.
• Apply causality analysis including counterfactual reasoning to discover root causes of risks from structured data.
• Apply and compare natural language analysis methods including lexical link analysis (LLA), AI transformers such as bidirectional encoder representations from transformers (BERT), retrieval augmented generation (RAG), knowledge graphs (KG), and large language models (LLMs) to extract themes and causes from unstructured data to characterize the risks. 
We will demonstrate the methodology using the use case from the mishap and incident reports of a marine transportation equipment, e.g., motorcycles or PMV2 and related data resources from the I Marine Expeditionary Force (IMEF).


Discovering Root Causes of Risks for Warfighting Systems & Human Factors Integration (WHI) Capabilities 

Neural network verification provides model robustness guarantees in the presence of noise. Our results show promise in using a combined approach to significantly boost verification efficiency and lower the threshold for operational viability of AI in mission- and safety-critical applications. We showcase two disparate, but complimentary, efforts for efficient formal verification and model robustness, resulting in increased performance and decreased verification effort.
Efficiency of this NP-complete problem is studied using α,β-CROWN. So-called smoothed models are benchmarked against their original versions.

Efficient Formal Verification of Neural Networks for Mission Assurance

Artificial Intelligence (AI) has become integral to critical applications, yet the lack of robust mechanisms for safety and explainability poses significant security concerns. To address this, ObjectSecurity LLC has developed a novel AI/ML trust analysis tool—funded through SBIR and internal R&D—that detects vulnerabilities in AI systems, enhances explainability, and enables targeted patching of identified weaknesses within an AI model.

This poster presents the technical mechanisms behind our approach, demonstrating how it detects and mitigates vulnerabilities in AI/ML models. The tool conducts a layer-by-layer analysis to identify key risks, including susceptibility to adversarial attacks and inefficiencies caused by overfitting. It improves model transparency by uncovering hidden relationships between tokens and optimizing neuron utilization, enabling targeted fine-tuning for maximum efficiency. By generating actionable metrics and applying custom LoRA masks, the tool facilitates rapid model adjustments, enhancing robustness, accuracy, and adaptability.

By fostering trust, confidence, and accountability, our solution supports the deployment of AI systems in high-stakes environments.

Vulnerability Analysis of AI/ML Systems

Background: This study explores factors influencing attrition in the U.S. Marine Corps by analyzing participant data from the Recruit Assessment Program (RAP) and employing machine learning techniques. 
Methods: The primary focus is to predict contract fulfillment, defined as attrition, fulfillment, or retention, and secondly, to identify predictors of different types of discharge. The study population included 53,156 male recruits at Marine Corps Recruit Depot, San Diego, who enlisted between 2013 and 2018, with a five-year follow-up period. 
Results: The analysis revealed that 15.0% of recruits did not meet their contractual obligations, 58.6% fulfilled them, and 26.4% exceeded them. Machine learning models identified in-service factors—such as promotion status, military occupation, and deployment rate—as the strongest predictors of both attrition and retention, whereas pre-service mental and behavioral health factors had minimal influence. Promotion status, as reflected by grade expectations, emerged as the most important predictor, with failure to meet early career promotions increasing the risk of attrition. In contrast, pre-service mental health variables, including depression, PTSD, ACE, and problematic anger, were not predictive of career outcomes. 
Conclusions: These findings suggest that in-service experiences and career progression play a more substantial role in determining long-term success in the military than pre-service mental health factors. These results highlight the potential for improving retention through enhanced support services during early military service rather than relying primarily on pre-service health screening.

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