United States

Sound transmission in the shallow ocean is highly dependent on the characteristics of the sea floor. An effective seabed, suitable for long-range transmission loss modeling, can be predicted with machine learning using passive sonar. Specifically, spectrograms of passing ships of opportunity (SOO) have been used in a ResNet-18 algorithm to classify an effective seabed type. One challenge with this approach is defining a catalog of seabed classes that are acoustically distinct. Currently, porosity is hypothesized to be the most significant parameter of the seabed; for the current work a catalog is defined with 15 seabed classes based on increasing porosity. This catalog is used to generate synthetic SOO spectrograms to train ResNet-18 to predict the seabed class. The trained algorithms are tested on measured data from the seabed characterization experiment (SBCEX 2017 and 2022) conducted in the New England mud patch. Preliminary results show that categorizing seabeds by porosity is a simpler and more robust approach than the previous catalog based on acousto-elastic parameters. In addition to investigating the seabed catalog, this work highlights the impact of water sound speed variability on deep learning results. Because the sound speed profiles vary between the 2017 and 2022 SBCEX data, differences between the performance of models trained on these datasets show the significance of understanding the water sound speed in order to accurately predict seabed type.

NAML 2026

Ship noise for deep learning seabed classification

ship noise

seabed classification

underwater acoustics

deep learning

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NAML 2021

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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. 



Due to climate change, the amount of ice cover in polar regions has become more variable. Because ice cover impacts sound propagation, proper sonar performance depends on an understanding of the presence or absence of ice. This project investigates the possibility of using deep learning to make this distinction. In preparation for ocean testing, initial measurements were collected in a laboratory tank. The experiment used a linear chirp from 10-50 kHz recorded at variable source and receiver positions in the tank. Approximately 150 liters of pebble ice were added to the water tank, and sound was recorded before ice was added, while it was present, and after it melted. From each one-second signal, the spectrogram was calculated and labeled based on the presence of ice. This labeled data was used to train a convolutional neural network (CNN) for binary classification for the presence or absence of ice. The trained CNN was applied to data collected on different days with different ambient noise conditions. The most successful CNN reached 81% accuracy on the unseen data. These results indicate the potential for deep learning to identify ice coverage in naval applications.

Acoustic Binary Classification of Ice Cover Conditions Using Deep Learning

General Problem: Future Distributed Maritime Operations (DMO) will rely on large-scale, heterogeneous swarms of unmanned systems (UxS). A critical barrier is achieving resilient autonomy in Disconnected, Intermittent, and Limited (DIL) environments. Standard distributed learning paradigms—such as Federated Learning—fail in these tactical scenarios for two reasons. First, they assume a fixed, reliable communication graph, which collapses under jamming. Second, they cannot handle "functional heterogeneity," where diverse agents (e.g., USVs vs. UAVs) possess incompatible state spaces, making simple parameter averaging mathematically invalid.

Proposed Framework: Sheaf Hypergraph Neural Networks. We propose a mathematically rigorous framework for decentralized representation learning based on Cellular Sheaves over Hypergraphs. Unlike standard Graph Neural Networks (GNNs) using scalar weights, we model the multi-agent system as a sheaf—a topological structure explicitly modeling algebraic constraints between local agent data.

Sheaf Diffusion for Resilient Consensus: We formulate coordination as a diffusion process governed by the Sheaf Laplacian. This operator drives the system toward a "harmonic" state (global consensus) via local exchanges. Because the diffusion is topological, it is inherently robust to node dropouts and dynamic topology changes common in contested spectrums.

Learning Geometric Constraints: We introduce a Sheaf Hypergraph Neural Network (SHNN) where the "communication protocol" is learned. The network learns Restriction Maps—linear transport operators that translate data between heterogeneous agents. During training, the model automatically discovers how to align feature spaces (e.g., sonar vs. optical) to create a unified common operating picture without a central fusion engine.

High-Order Hypergraph Modeling: We utilize hypergraphs to capture multi-way interactions (e.g., triangulation tasks), ensuring the learning process respects high-order mission dependencies rather than reducing them to pairwise links.

Naval Applications: This framework offers foundational technology for NIWC/NRL priorities in trusted autonomy:

Swarm Robotics and Heterogeneous Teaming: For Distributed Autonomous Systems (DAS), this replaces heuristic "flocking" rules with topological guarantees. It enables functionally diverse swarms to maintain formation and allocate tasks even when 50% of communication links are severed.

Collaborative Spectrum Sensing (EW): Distributed agents collaboratively classify hostile RF emitters. The sheaf framework aggregates partial signal features into a global result without transmitting raw IQ data, significantly reducing bandwidth.

Multi-Modal Sensor Fusion (ISR): A decentralized training framework for joint target recognition. Learned restriction maps mathematically ensure that acoustic, thermal, and visual features are fused consistently across the fleet.

Sheaf-Theoretic Hypergraphs for Resilient Distributed Autonomy and Learning in Contested Environments

Division 584 is developing several applied artificial intelligence (AI) capabilities to support cyber security engineering, including the Policy Related Assessment of Controls using Artificial Intelligence (PRAC-AI) and the Controlled Unclassified Information Categorizer (CUICAT); as well as the Technology Transition Classifier (TTC), the Technical Library Chatbot (TCLB) and AI@FlankSpeed. PRAC-AI, a Naval Innovation Science and Engineering (NISE) in NIWC PAC, ingests Risk Management Framework (RMF) test result exports (TREs), uses Python and the Pandas library to filter inherited controls, retrieves authoritative configuration information from policy documents, and generates draft test results using generative artificial intelligence (GenAI) and retrieval-augmented generation (RAG). The Information System Security Engineer (ISSE) maintains responsibility for approval, but PRAC-AI significantly reduces writing and formatting effort and improves consistency across assessments. CUI-CAT employ fine-tuned Bidirectional Encoder Representations from Transformers (BERT) models to detect and categorize all 126 categories of CUI, whereas the TTC categorizes weekly highlights into one of nine NAVWAR Technology Transition categories. The TLCB uses a lightweight retrieval-augmented approach to allow personnel to interact with Naval Information Warfare Center (NIWC) Pacific publications. In parallel, the division is collaborating with NearlyHumanAI through a Small Business Innovation Research (SBIR) effort on AI@FlankSpeed, an Azure-hosted, FlankSpeed-attached platform that builds custom generative AI workflows and exposes application programming interfaces (APIs) for integration with FlankSpeed applications and other cloud-hosted services.

Enabling Consumption of AI/ML Use Cases in the IL5 Environment

Naval tasking requires an intense focus that can burden cognitive resources and may reduce warfighter efficiency and performance accuracy. Understanding cognitive workload (CW) through brain computer interface (BCI) technology may provide solutions to optimize performance of operational procedures. This project reflects an ongoing effort [1] to measure and manipulate cognition through standard psychological tasks to develop a real-time CW classification approach. Here, we report on classifier performance of CW using electroencephalogram (EEG) cognitive task datasets for human in the loop optimization of warfighter performance. We created software (modified from a public source [2]) to reproduce a MATBII [3] cognitive workload experiment, where users perform a multi-tasking exercise with varying cognitive difficulty. We administered an experimental design to collect low and high CW data from our research team. We generated relevant plots to analyze our in-house data against highly processed public datasets. We structured and processed EEG data according to methods that can be conducted for real-time applications, including procedures for noisy EEG channel interpolation. In our search for an optimal real-time model, we tested multiple CNNs with and without pretraining on externally sourced CW datasets [4], using leave-one-out cross-validation for classification accuracy. Following our simulated real-time approach during MATBII task performance, we were able to achieve a mean cross-subject accuracy of 65% (range: 54%-70%) for binary classification of high vs low CW on our small dataset. Notably, we can obtain higher accuracies (mean: 73%; range: 66%-85%) by introducing additional subject-specific training and are currently researching methods to implement this reliably in real-time systems. Altogether, these results directly inform development of real-time BCIs for Navy use, and specifically for use cases that continuously demand and fatigue cognitive resources.
[1] Onners, J., Ronquillo, N., Trammel, T., Houst, D., Sengphanith, R., Wymbs, N. (2025, February 25). Real Time Cognitive Engagement Classification using CNNs. [Poster presentation]. NAML 2025, San Diego, CA, United States. https://doi.org/10.48448/tpkd-xg20
[2] Cegarra, J., Valéry, B., Avril, E., Calmettes, C., & Navarro, J. (2020) OpenMATB: A Multi-Attribute Task Battery promoting task customization, software extensibility and experiment replicability. Behavior Research Methods, 52, 1980–1990. https://doi.org/10.3758/s13428-020-01364-w
[3] Comstock Jr J. R., Arnegard R. J. (1992). The multi-attribute task battery for human operator workload and strategic behavior research. Available at: https://ntrs.nasa.gov/citations/19920007912.Lastedaccessthe02/02/2024
[4] Hinss, M.F., Jahanpour, E.S., Somon, B. et al. Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications. Sci Data 10, 85 (2023). https://doi.org/10.1038/s41597-022-01898-y

Cognitive Workload Estimation of Noisy EEG using ML

Problem Context
Machine learning efforts often begin not with “big data,” but with small, imperfect data. Legacy systems store information in non-machine-readable formats, and institutional repositories suffer from poor maintenance and broken links. These challenges compound when integrating data sources with misaligned data definitions, increasing ML workflow complexity. This work demonstrates a practical framework for making small, messy datasets analytically usable through careful preprocessing, transformation, and model selection.

Technical Approach
This paper demonstrates techniques for integrating heterogeneous government datasets to address foundational issues: (1) geospatial crosswalk construction using representative-point projection rather than geometric centroids for correct tract-to-region assignment; (2) variance stabilization for bounded, right-skewed rate data using Box-Cox transformation; (3) multicollinearity reduction in compositional features using isometric and additive log-ratio transformations; (4) model evaluation via stratified 10-fold cross-validation to address sample-size constraints, comparing linear regression and classification approaches. Together, these transformations mitigate spatial misalignment, heteroscedasticity, and multicollinearity while retaining observations—avoiding the need to remove influential cases (e.g., via Cook’s distance) or drop collinear features.

Results
Linear regression models achieved poor explanatory power (R² = 0.47 ± 0.28) with high cross-validation variance, indicating that continuous rate prediction is unreliable under current data constraints. Reframing the problem as binary classification (high-risk vs. baseline regions using 75th-percentile thresholds) yielded 91 % ± 4 % accuracy with F1 = 0.74 ± 0.16, demonstrating stable performance despite class imbalance. SHAP analysis provided ranked feature importance, enabling interpretable model outputs suitable for government decision-making contexts.

Implications
A well-designed pipeline combining careful preprocessing with appropriate model selection can yield actionable insights despite challenging data conditions. Geospatial crosswalk construction, variance stabilization, and compositional feature handling enabled meaningful analysis where raw data integration would have failed. While regression could not produce reliable forecasts (R² = 0.47 ± 0.28), classification identified high-risk regions (91 % ± 4 % accuracy), supporting prioritization and resource-allocation decisions. These methods are relevant for domains facing similar data constraints. The dataset and crosswalk methodology are publicly available on Kaggle for reproducible validation and adaptation.

David Elgas
MS Data Science Candidate, Harvard University
BSAE, Aerospace Engineering, San Diego State University


Making Imperfect Data Work: A Practical ML Framework for Small-Sample, Multi-Source Data Integration

This project leveraging AI for Mission Assurance and Quality Management Systems in the Navy proposes the development of an advanced artificial intelligence (AI) framework to enhance mission assurance and quality management systems (QMS) within the Navy. By leveraging cutting-edge technologies such as natural language processing (NLP), large language models (LLMs), and specialized AI agents, the framework will address critical challenges in quality assurance, operational readiness, and controlled unclassified information (CUI) management. The solution emphasizes the secure handling of proprietary information, specifications, and requirements, ensuring compliance with authority to operate (ATO) standards.
Fine-tuned LLMs will be central to the framework, enabling domain-specific capabilities such as summarization, topic modeling, and document comparison (e.g., specification-to-functionality alignment). These models will be trained on curated datasets containing CUI, proprietary data, and technical documentation, ensuring relevance and accuracy. Vector databases will facilitate efficient inference and retrieval of embeddings, supporting rapid analysis and decision-making. Specialized agent assistance will automate routine tasks, such as data curation and wrangling, while providing actionable insights to Navy personnel.
To meet the Navy's stringent security and operational requirements, the AI solution will be deployed as a containerized application optimized for small footprint and hardware constraints. On-premises deployment will ensure data sovereignty and minimize exposure to external threats. Prompt engineering techniques will enhance user interactions, enabling tailored responses and improving human-machine collaboration.
The framework addresses key challenges in mission assurance and QMS, including managing large volumes of sensitive data, ensuring alignment between specifications and functionality, and maintaining operational readiness. By automating quality assurance processes, enforcing standardization, and delivering real-time insights, the project will transform the Navy's ability to ensure reliability, efficiency, and security in its operations. This innovative approach positions the Navy to leverage AI for strategic advantage in modern warfare.

Artificial Intelligence for Mission Assurance

The fusion of Artificial Intelligence (AI) with signal processing has highlighted signal labeling as an essential part of the workflow. This poster/presentation aims to explore the methodologies in signal labeling and the integration of deep learning techniques to automate and optimize the process, while focusing on an example of automated labeling of flight maneuvers. While some flight test data sets are already labeled, a large set of historical data may be missing or have less accurate labels. Using AI/ML methods to iteratively label these data sets allows the recovery of this historic data for use in analysis.

Enhancing AI Applications with Automated Signal Labeling – A Flight Test Data Application

Integrated photonics is an innovative technology that merges optical components with traditional electronic circuits, leveraging mature, cost-effective semiconductor manufacturing processes to enable high-speed data transmission and lower power consumption than conventional electrical interconnects. Currently, the market is experiencing robust growth, primarily in data centers and high-performance computing, where photonics-based transceivers are essential for meeting the escalating bandwidth demands of cloud services and AI workloads. Looking ahead, envisioned applications extend beyond telecommunications to include highly integrated solutions like co-packaged optics (CPO) placed directly alongside silicon chips and analog computation systems for lightspeed, energy-efficient matrix multiplication. However, scaling such systems does not come without challenges, such as modulation, loss, noise, and footprint. To address such concerns, we have employed advanced numerical optimization techniques, commonly called inverse design, to the engineering of photonic devices and circuits. Inverse design methods can be used to optimize a user-defined photonic metric with respect to thousands of design parameters using computationally efficient algorithms such as the adjoint state method, leading to, for example, photonic devices and circuits that have near-unity energy efficiency and minimal chip footprints. Furthermore, recent revelations in statistical learning methods such as artificial intelligence and scientific machine learning can be used to enhance inverse design algorithms. We will present our work on integrating the latest automatic differentiation tools into photonic inverse design workflows, constructing physics-informed neural operators as data-free surrogate partial differential equation (PDE) models, and ultra sample efficient optimization methods leveraging low-accuracy surrogates.

Engineering State-of-the-Art Photonic Devices and Circuits with AI-Enhanced Inverse Design

Major theoretical and experimental advances in designing controllable quantum systems have brought us closer to Richard Feynman's vision of using quantum mechanics to simulate nature. Today, we can access up to hundreds of qubits across a range of device types, including superconducting qubits, neutral atoms, trapped ions, photonics, and more. But to unlock the full potential of quantum computing (QC)—meaningful speedups for real-world problems like simulating chemistry and materials science—we need to scale these quantum systems to orders of magnitude more qubits and do so cost-effectively. This milestone, known as quantum utility, marks the point where quantum computers become truly useful. To achieve quantum utility, it will be necessary to tightly integrate quantum processors (QPUs) with classical, high-performance computing (HPC) systems. This is because, rather than acting as general-purpose processors, QPUs function more like accelerators or coprocessors, designed to tackle highly specialized tasks that are beyond the reach of classical computers. This hybrid HPC-QC model will be essential not only in the near term—the noisy, intermediate-scale quantum (NISQ) era—but also for future fault-tolerant quantum computation (FTQC), as quantum error-correction schemes will rely heavily on classical co-processors. To achieve so-called utility-scale quantum computing, it will be necessary to integrate with existing heterogeneous HPC infrastructures and develop a hybrid quantum-classical full computing stack. In other words, we will need to build a quantum supercomputer. 

We present a full quantum-classical stack with tight integration of HPC and QC in multiple layers. At the highest layer, we extend familiar HPC programming environments to include QC capabilities, making it easier for developers to run quantum tasks alongside classical, high-performance ones. This includes a quantum accelerator API, which allows seamless execution of quantum algorithms, and a smart scheduler that efficiently distributes quantum workloads across available resources. At a lower level, we provide an HPC solution for circuit compilation and real-time quantum error correction that uses known hardware profiles to synthesize optimized fault-tolerant circuits. Integration at this layer will require fast communication between the quantum and classical components, and can be enabled by ultra-low latency interconnects. Novel algorithms and heterogeneous coprocessing—including CPU/GPUs, QPUs, and probabilistic processing units (PPUs)—allow the system to partition a particular problem into subproblems and send them out to the right coprocessors, enabling even more efficient execution. To aid in designing and evaluating the hybrid stack, we provide resource estimates for utility-scale (FTQC) problems in materials science as well as demonstrate HPC-QC integration workflows for several near-term (NISQ) applications.

How to Build a Quantum Supercomputer: Enabling Utility-Scale Quantum Computing with HPC-QC Integration

Predictive maintenance is a high value application of machine learning, enabling early detection of degradation and reducing unscheduled failures. Across domains such as rotating machinery, HVAC systems, batteries, and unmanned platforms, ML driven programs report 18–25% reductions in sustainment cost and up to 40% reductions in downtime in mature systems. Naval and industrial assets, however, often operate in distributed, communication-limited environments where centralized training or cloud-based inference is impractical.

Distributed Predictive Maintenance (DPM) addresses this constraint by enabling edge nodes to analyze local sensor data, detect anomalies, and forecast failures using onboard ML models. Rather than sending raw data to a central server, nodes exchange compact model updates that allow a global model to emerge collaboratively. Our approach employs a fully decentralized learning algorithm, removing the parameter-server dependencies common in federated learning and enabling low latency inference even when networks are degraded, partitioned, or intermittently connected.

Communication efficiency is a central challenge: scalable model exchange requires many nodes to share updates without overwhelming RF resources. Traditional networks assume orthogonal channels per device, which becomes infeasible as sensor density grows to hundreds or thousands of shipboard or expeditionary IIoT nodes. To address this, Trabus Technologies developed the Spectrally Efficient Peer-to-Peer (SEPP) waveform, which allows many nodes to communicate simultaneously within the same band and reduces the overhead associated with distributed optimization.

Building on this capability, SEPP-DPM integrates SEPP with decentralized learning algorithms leveraging current research from Chris Brinton at Purdue University in topology-aware and consensus-based optimization. This enables autonomous condition-based maintenance driven by real-time performance trends rather than fixed schedules. The architecture remains resilient to communication loss, node churn, and non-IID data distributions typical of naval environments.

A key innovation is SEPP-DPM’s dual-model architecture:
• the Distributed System Health Model (DSHM), an online trained representation of equipment behavior; and
• the Distributed Communication Channel Model (DCCM), which captures time varying RF conditions that influence model-exchange strategies.

These models co-evolve, enabling joint optimization over sensing, communication, and learning. Nodes adapt update frequency, peer selection, and aggregation schedules based on channel estimates, yielding an ML system that is both data efficient and communication aware.

By unifying decentralized learning with efficient communications, SEPP-DPM provides a scalable framework for distributed fault detection, anomaly prediction, and resilience in naval environments, supporting higher availability and ML driven decision superiority at the tactical edge.


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