United States

For weather-informed ship route planning, optimizing for fuel efficiency entails predicting how the ship will react to the ocean environment during a voyage. Fuel consumption over a route is strongly dependent on ship resistance, which is primarily due to calm water resistance and added resistance in waves. Physics-based models of ship hydrodynamics can be employed to compute resistance as function of the hull form geometry, and ocean wave conditions. However, development of these models entails a significant amount of detailed information about the ship geometry, dimensions, and loading conditions, which are often not readily available. Additionally, there is a significant computational cost associated with the development of physics-based models. To address these challenges, this study investigates the feasibility of robust data generation for model training as part of the Machine Learning Surface ship Resistance estimation Framework (ML-SURF). This framework is focused on developing AI-enabled models to rapidly estimate calm water resistance and added resistance in waves based on a limited set of ship information.

The first step in this framework entails rapid generation of a wide range of ship geometries resulting in approximately 2,000 hull forms. Hull form geometries are generated using Rhinoceros 3D (Rhino), which is an advanced computer-aided design and graphics tool. In Rhino, this entails manipulation of the control points of a baseline parent design to generate new ship geometries based on a set of hull shaping parameters. Once the ship geometries are generated, calm water resistance and added wave resistance is computed for each respective hull form as function of large set of ocean conditions (wave height, modal period, relative wave heading), and ship speeds. Resistance predictions are generated using fast, reduced-order physics-based modeling and simulations. The resultant dataset covers a significant variety of displacement mono-hulls for a wide range of ship shaping and sizing parameters, which is a key enabler for robust model development via machine learning. As part of ML-SURF, a companion study is focusing on exploratory analysis and model training with this dat

NAML 2025

Ship Resistance Estimation Framework with Machine Learning: Data Generation

ship resistance

data generation

machine learning

### 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
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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

“Harnessing Spatial AI for Advanced Geospatial Intelligence”
The rapid evolution of artificial intelligence and geospatial technology has paved the way for unprecedented advancements in various sectors, and in an era marked by complex geopolitical dynamics and continuous innovation, the U.S. Navy's strategic operations increasingly rely on advanced data analysis and intelligence capabilities. The introduction of spatial artificial intelligent within the machine learning ecosystem offers a transformative approach to processing and interpreting geospatial information. Oracle will present how spatial artificial intelligent empowers the U.S. Navy to enhance its intelligence, surveillance, and reconnaissance operations, thereby improving strategic decision-making and operational effectiveness.
Spatial AI equips the U.S. Navy with sophisticated geospatial machine learning algorithms that are designed for the detection of spatial patterns, prediction of outcomes, and classification of spatial data. By leveraging these advanced capabilities, naval personnel can efficiently and more accurately analyze vast amounts of geospatial data, which is essential for identifying potential threats, monitoring maritime activities, and supporting national security. The comprehensive suite of algorithms provided by spatial AI is designed to analyze vector data and location-based events, facilitating actionable insights. The end-to-end workflow capabilities offered by Spatial AI, encompassing data preparation, model training, and application, enabling the U.S. Navy to fully harness the power of spatial data and enhance their operational effectiveness. 
Spatial AI can be seamlessly integrated with various existing systems and processes, offering the potential to enhance the U.S. Navy’s strategic operations. For instance, by leveraging advanced spatial AI technology’s comprehensive workflow capabilities, which include training spatial machine learning models, and applying these models to various applications, the U.S. Navy can enhance the integration of AI into the existing systems. This integration allows for the consolidation of diverse geospatial data sources, enabling the U.S. Navy to uncover hidden trends and patterns that might otherwise go unnoticed. 
In practical terms, spatial AI can enhance the U.S. Navy's ability to conduct maritime domain awareness operations by providing precise and timely geospatial intelligence. This capability can be applied to monitoring illegal activities, such as smuggling and unauthorized fishing, as well as for planning and executing naval operations with greater accuracy. Additionally, spatial AI supports the U.S. Navy's efforts in disaster response and humanitarian assistance by enabling rapid assessment and analysis of affected areas.
In summary, the implementation of spatial AI within the U.S. Navy represents a significant advancement in geospatial intelligence and strategic planning. By harnessing the power of spatial AI, the U.S. Navy can improve its situational awareness, enhance international cooperation, and make well-informed decisions in an increasingly complex and interconnected world.

Harnessing Spatial AI for Advanced Geospatial Intelligence

As with any tool, there are risks to misusing Large Language Models (LLMs). However, unlike with standard tools, even correct use of LLMs can cause harm – consider hallucinations: unfounded replies that are simply made up. 

Technological changes, such as upgrading a model to a newer, more performant version are relatively easy. Changing human behavior – how people use these tools – is much harder: established usage patterns can be hard to budge. 

To address this issue, our team created an LLM demo website for a client founded on human design principles to prioritize responsible usage. We emphasize the following features:
• Provide guidance on how models can be biased and can hallucinate 
• Provide information on which LLM was chosen and why
• Explain how the model was trained
• Reveal what information is saved, and what is not
• Calculate and provide automated diagnostics of model performance in real-time 
• Empower users to upload their own documents to see how well the model works

We believe this is a differentiated approach that focuses on the less malleable, but more important aspect of LLM interaction: the user. We believe this approach could and should be more widely used throughout the U.S. Government, and particularly within the Department of Defense (DoD). 


Human Centric Design to Foster Responsible LLM Use

The objective of this research was to evaluate machine learning-based optical character recognition (OCR) to automate recognition of aircraft side numbers on the carrier flight deck. In this research, we developed a software pipeline that utilizes a pan-tilt-zoom (PTZ) camera to achieve this automation. The pipeline detects all aircraft in view, a PTZ camera to obtain high resolution images of each aircraft, then OCR to extract the side number text. The final algorithm is demonstrated using a combination of stationary aircraft.


Automated Aircraft Sidenumber Recognition using Computer Vision for Naval Aircraft Logistics

Spiking Neural Networks (SNNs) [1], which are inspired by the functioning of the brain, offer a promising approach to artificial intelligence with significant advantages in energy efficiency. This makes SNNs particularly suitable for use in underwater edge devices, such as unmanned underwater vehicles (UUVs), which are often constrained by limited battery capacity. However, current SNN-based methods for underwater acoustic data representation learning remain relatively underexplored. In this work, we propose to investigate the use of Spike Timing Dependent Plasticity (STDP) [2] within SNNs for effectively analyzing underwater acoustic data. Our objective is to develop a representation learning method that requires minimal training data and enables energy-efficient data processing on UUVs in challenging underwater environments.

Underwater Acoustic Data Representation Learning with Spike Timing Dependent Plasticity


COSMOS is a state-of-the-art government cloud infrastructure. See firsthand how its capabilities can support DoD customers, boost efficiency, propel AI/ML adoption forward, and drive mission success.

COSMOS: Moving Mission Light Years Ahead

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

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

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

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 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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Harnessing Spatial AI for Advanced Geospatial Intelligence

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