United States

Naval tasking and new technologies often require intense cognitive engagement, but it has proven challenging to provide an objective evaluation of how these technological advances affect the warfighter user and thus how they affect decision-optimization. In this work, we address these challenges by using an AI/ML neuro-ergonomic approach that combines state-of-the-art concepts in brain computer interface (BCI) technology with cognitive state detection. We, the NeuroML Research Team of NIWC Pacific’s Intelligent Sensing Branch, build upon research in which we have addressed multiple key challenges contributing to the development of a passive BCI (pBCI), by developing a demonstration of a real-time cognitive workload classifier.

NAML 2025

Real Time Cognitive Engagement Classification using CNNs

### Welcome to the 10th 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.
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* 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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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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Registration for this workshop has now closed.

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.

SeaVision is the U.S. Navy's unclassified data aggregation, data visualization, and sharing tool of tracks for maritime domain awareness (MDA), incorporating AIS, commercial satellite, RADAR, and other feeds into a unified picture. Currently, there is a need for higher-level sensemaking capabilities, including warning and event modules, to identify high-risk vessels and automate understanding of behaviors across an area of responsiblity (AOR). In this poster presentation, we demonstrate the process of identifying a high-risk vessel from a slice of historical data based on data processed from the SeaVision and highlight a hybrid-HPC/cloud development workflow that our team leverages to enable the cost-effective development and deployment of machine learning algorithms applied to these large-scale data. Finally, our team describes how system modeling techniques are leveraged to drive stakeholder engagment throughout the project.

MUSES: Multimodal Sensemaking in SeaVision

We investigate a real-time remote inference system where multiple correlated sources transmit observations over a communication channel to a receiver. The receiver utilizes these observations to infer multiple time-varying targets. Due to limited communication resources, the delivered observations may not be fresh. To quantify data freshness, we employ the Age of Information (AoI) metric. To minimize the inference error, we aim to design a signal-agnostic scheduling policy that leverages AoI without requiring knowledge of the actual target values or the source observations. This scheduling problem is a restless multi-armed bandit (RMAB) problem with a non-separable penalty function. Unlike traditional RMABs, the correlation among sources introduces a unique challenge: the penalty function of each source depends on the AoI of other correlated sources, preventing decomposition of the problem into multiple independent Markov Decision Processes (MDPs), a key step in applying traditional RMAB solutions. To address this, we propose a novel approach by approximating the penalty function of each source and establish an analytical bound on the approximation error. We then develop scheduling policies for two scenarios: (i) full knowledge of the penalty functions and signal distributions and (ii) no knowledge of the penalty functions and signal distributions. For the case of known penalty functions, we present a near-asymptotically optimal gain index-based policy. For the case of unknown penalty functions and signal distributions, we develop an online learning approach that utilizes feedback of the inference loss to learn an online Maximum Gain First (MGF) policy. Simulation results demonstrate the effectiveness of our proposed policies in minimizing inference error and achieving scalability in the number of sources.

AoI-based Scheduling of Correlated Sources for Timely Inference

Climate modelling has long been crucial to disaster management, protecting existing infrastructure, and tactical success, with weather forecasts informing critical military decisions since D-day. In the modern era, the vast majority of weather modelling relies on centralized numerical weather prediction (NWP) models which are incredibly expensive to run, develop, and ultimately have limited performance especially at longer timescales. 

However, nascent AI approaches have recently beat traditional modelling approaches across metrics, achieving greater accuracy for far less training, inference, and development time. ClimAI presents a novel geospatial foundation model (GFM) architecture improving the existing vision transformer (ViT) patch embedding. The ViT architecture uses patches representing regions of space and time over the input data. However, weather reanalysis datasets, such as ERA5, provide weather data as projections of the Earth’s spheroidal topology onto a rectangle. This introduces both position-dependent warping and an arbitrary seam at the 180° line of longitude. This may be eliminated by considering the topology of the ERA5 dataset as a cylinder, achieving a general topologically-informed patch embedding that is more robust and performant, ensuring consistent patch representation across latitudes and mitigating the 180° line of longitude seam in the Pacific. Further, we demonstrate that by selecting patches in spherical coordinates, the model weights geospatial weather data equally across the globe, improving the model’s generalization. 

GFM models are not only more accurate, but orders of magnitude cheaper to use than traditional NWP approaches, with our model being possible to perform inference in seconds on even CPU-only compute. Our team and NOAA stakeholders are excited about the future of AI enabling distributed and domain-specific geospatial modelling for specific needs without relying on requests to a centralized agency, such as NOAA’s existing supercompute infrastructure, enabling generalizable on-prem geospatial time-series forecasts.

ClimAI: Improving Patch Embeddings for Geospatial Foundation Models

Many of the current state of the art Intelligent Autonomous Systems (IAS) and robotics systems use Inertial Measurement Units (IMU) for navigation and control. The magnitude of the effect is unknown leaving a large gap in knowledge. We present our findings of a mobile robot operating on a ship deck that is underway. We operated our mobile robot at a Coastal Trident event collecting both GPS and IMU data while the mobile robot drove around the ship deck. This data give insight into how we can be to develop methods for accurate state estimation. This is the first critical step in developing robust controller for autonomous robots. The results show the robots reaction as the ship moves and sways. We compare the IMU on the robot to that of the ship to cancel out the motion of the ship.

Ship Experiments for Autonomous Locomotion and Euler Guidance Systems  (SEA LEGS) 

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.

Real-Time Plume Estimation with an Autonomous Underwater Vehicle

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.

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