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NAML 2025

COSMOS: Moving Mission Light Years Ahead

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### 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. 
<br>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
<br><br>

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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.

poster

Intelligent autonomous systems (IAS) are crucial for enabling the Department of Defense (DoD) to operate effectively in complex and contested environments while reducing risks to personnel and mission success. Maritime domain awareness (MDA) can be significantly enhanced by deploying numerous, distributed, and attritable autonomous platforms capable of adapting to dynamic, constrained, or denied environments.

This research explores autonomous, multi-agent solutions for maintaining resilient communications in these types of scenarios. Our approach focuses on dynamically positioning multiple heterogeneous unmanned platforms to optimize mesh network connectivity in order to maintain connectivity with the fleet. We employ multi-agent reinforcement learning (MARL) algorithms to jointly maximize search effectiveness and network link strength.

JAZZ: Autonomous Multi-Agent Systems for Maritime Network Coordination and Resiliency

This talk is going to discuss recent advances and next steps for assured spectrum perception using AI/ML techniques in tactical environments.

Resilient Dynamic AI/ML for Assured Spectrum Perception in Tactical Environments: Recent Advances, Existing Challenges and Next Steps

Radar-based automatic target recognition (ATR) of UAVs is critical for security and defense. UAV recognition is commonly done using micro-Doppler (μD) signatures which are characterized by Helicopter Motor Rotation (HERM) lines that appear parallel to the main UAV return and are unique per the number of and rotation rate of propellers. This paper addresses two challenges: (1) accurate synthesis of target signatures and (2) development of classifiers robust to dynamic clutter environments, which have a different distribution than the training dataset. 

The first challenge to deep learning-based ATR is a lack of adequate amounts of quality data for training. It is often not feasible to acquire data in operational areas of interest, consequently synthetic training data generation has been a topic of intense research. However, current methods do not account for sensor-specific, target-specific, or environment-specific characteristics, which may also vary with time. Generative Adversarial Networks (GANs) are an alternative way to synthesize data from a few samples of real data, which can capture such dynamic factors. However, our prior work has shown that GANs synthesize kinematically impossible target signatures, which can degrade classification accuracy. In synthetic UAV signatures, HERM lines are often distorted, duplicated, or the trajectories are corrupted. The second challenge is that data acquired in real operational environments often has a signal-to-interference-plus-noise ratio (SINR) than the training dataset which degrades target recognition.

We propose a novel physics-based approach that addresses both UAV signature synthesis and classification in dynamic environments. First, we develop a Physics-Guided Semantic Segmentation (PhG-SS) network which uses U-Net as a backbone and is trained on low-fidelity physics-based UAV simulations. To then synthesize large datasets for training which account for the inherent kinematics of UAVs, we propose a Semantic-Segmentation GAN (SS-GAN) in which PhG-SS is added as an auxiliary discriminator branch. We show that this results in synthesis of UAV signatures with kinematically accurate HERM lines. When SS-GAN data is used in training, we found that it improved the classification accuracy of a convolutional neural network (CNN) even when there is mismatch between the SINR of training and test sets. Second, we propose a Semantic-Segmentation DNN (SS-DNN) in which PhG-SS is integrated as signal conditioning prior to convolutional layers. Our results show that the combination of training the SS-DNN using SS-GAN signatures synthesized from high SINR data results in the highest classification accuracy when testing on UAV signatures with low SINR. The implications of these results are that our proposed approach can enable target recognition in new environments from which no a priori training data is available.


Physics-Guided Semantic Segmentation for UAV Radar Micro-Doppler Synthesis and Classification in New Environments

Understanding the agent’s learning process, particularly the factors that contributeto its success or failure post-training, is crucial for comprehending the rationalebehind the agent’s decision-making process. Prior methods clarify the learningprocess by creating a structural causal model (SCM) or visually representing thedistribution of value functions. Nevertheless, these approaches have constraintsas they exclusively function in 2D-environments or with uncomplex transitiondynamics. Understanding the agent’s learning process in complex environmentsor tasks is more challenging. In this paper, we propose REVEAL-IT, a novelframework for explaining the learning process of an agent in complex environments.Initially, we visualize the policy structure and the agent’s learning process forvarious training tasks. By visualizing these findings, we can understand how mucha particular training task or stage affects the agent’s performance in the test. Then,a GNN-based explainer learns to highlight the most important section of the policy,providing a more clear and robust explanation of the agent’s learning process.The experiments demonstrate that explanations derived from this framework caneffectively help optimize the training tasks, resulting in improved learning efficiencyand final performance

REVEAL-IT: REinforcement learning with Visibility of Evolving Agent poLicy for InTerpretability

Station-Keeping short-duration high-altitude balloons (HABs) in a region of interest is a challenging path-planning problem due to partially observable, complex, and dynamic wind flows. Deep reinforcement learning is a popular strategy for solving the station-keeping problem. A custom simulation environment was developed to train and evaluate Deep Q-Learning (DQN) for short-duration HAB agents in the simulation. To train the agents on realistic winds, synthetic wind forecasts were generated from aggregated historical radiosonde data to apply horizontal kinematics to simulated agents. The synthetic forecasts were closely correlated with ECWMF ERA5 Reanalysis forecasts, providing a realistic simulated wind field and seasonal and altitudinal variances between the wind models. DQN HAB agents were then trained and evaluated across different seasonal months. To highlight differences and trends in months with vastly different wind fields, a Forecast Score algorithm was introduced to independently classify forecasts based on wind diversity, and trends between station-keeping success and the Forecast Score were evaluated across all seasons.

Seasonal Station Keeping of Short-Duration HABs using Deep Reinforcement Learning (DRL)

Knowledge of underwater acoustics in diverse oceanic environments is key to accurate analysis of sonar systems used for Air Anti-Submarine Warfare (ASW) simulations. By identifying variations in underwater sound propagation, analysts score the acoustic abilities of sonar tools according to their performance in distinct regions. Since these variations are sensitive to environmental changes, leveraging known underwater properties to group oceanic regions could be used to improve acoustic analysis and inform decision makers.

Ocean Provincing Tool

Project Nereus explores the integration of OpenAI Swarm and agentic AI to coordinate Autonomous Underwater Vehicles (AUVs) for enhanced undersea operations, particularly in the context of Subsea and Seabed Warfare (SSW). The system aims to enable AUVs to collaborate and share information effectively, leading to improved efficiency in tasks such as exploration, mapping, anomaly detection, and underwater target threat detection, directly supporting the need for maintaining US overmatch in the undersea domain. This approach addresses challenges such as limited communication, unpredictable conditions, and real-time adaptation needs in underwater environments. The project will involve designing AUV agents with functionalities for movement, sensor data acquisition, communication, and task execution. It will also incorporate a realistic simulation environment for testing and refining the system, leveraging tools like Gazebo or UUV Simulator. The project aims to demonstrate the feasibility of using OpenAI Swarm for undersea operations, develop and test AI agent behaviors, and create a realistic simulation environment for evaluating system performance. Ultimately, Nereus aims to provide an integrated solution that can be deployed on unmanned systems to support naval undersea warfare operations, directly contributing to the goals outlined for SSW.

Project Nereus: OpenAI Swarm for Undersea Autonomous Operations

Digital twins provide real-time, high-fidelity predictions that are essential for fatigue
analysis, assessing the integrity of components under extreme loading, and designin
resilient materials for aerospace, automotive, and civil infrastructure. Accurate modeling of material response to deformation is critical to ensure safety and performance across industries. For this purpose, machine learning (ML) has emerged as a promising alternative to physics-based simulations, which often rely on constitutive relations that do not fully capture all processes present in real-world scenarios. However, ML models lacking  physically principled design struggle to generalize effectively, particularly in scenarios involving complex strain histories and multiaxial loading conditions.

This work demonstrates the advantage of enforcing material objectivity, a fundamental physical constraint that governs how the stress tensor transforms under rotations and reflections. We compare a standard convolutional neural network to an equivariant convolutional neural network, which exactly respects O(3) symmetry at the architectural level. Both models are trained on the strain to stress relationship of J2 plasticity; however, only the equivariant model is guaranteed to learn mappings consistent with the principle of material objectivity. As a result, our equivariant model achieves superior prediction accuracy across complex deformation scenarios.

Learning Metal Plasticity with an Equivariant Neural Network

The cornerstone of this effort is the integration of taxonomies and ontologies to enable semantic enrichment and improved data interoperability. This approach will facilitate the efficient discovery of data assets, making it easier for users to connect diverse datasets, regardless of their source or structure. Specifically, the semantic enrichment of existing datasets will enable compatibility with AI tools such as Large Language Models.

Enabling Open Discovery and Decision-Making with a Knowledge Mesh for Environmental Intelligence

The convergence of technologies—from foundation AI models to diverse sensors and actuators to ubiquitous connectivity—is transforming the nature of interactions in the physical and digital world. People have accelerated their collaborative connections and productivity through digital and immersive technologies, no longer limited by geography or language or access. Humans have also leveraged and interacted with AI in many different forms, with the advent of hyperscale AI models (i.e., large language models) forever changing (and at an ever-astonishing pace) the nature of human-AI teams, realized in this era of the AI “copilot.” Similarly, robotics and automation technologies now afford greater opportunities to work with and/or near humans, allowing for increasingly collaborative physical robots to dramatically impact real-world activities. It is the compounding effect of enabling all three capabilities, each complementary to one another in valuable ways, and we envision the triad formed by human-robot-AI teams as revolutionizing the future of society, the economy, and of technology.

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JAZZ: Autonomous Multi-Agent Systems for Maritime Network Coordination and Resiliency

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