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  • USACE hosts open house April 30 in Sturbridge, Mass., for East Brimfield Lake Master Plan revision

    The U.S. Army Corps of Engineers, New England District will host an open house April 30, 2024, in

  • USACE, City of Cincinnati developing plan to improve Cincinnati’s riverfront

    The U.S. Army Corps of Engineers Louisville District is working in coordination with the City of Cincinnati and Hamilton County to help improve Cincinnati’s Riverfront along the Ohio River in downtown Cincinnati, Ohio. USACE and the Cincinnati Park Board are partnering on a $2.5 million feasibility study to improve and revitalize the Ohio River’s edge along Smale Riverfront Park.

  • Media Advisory: Atlantic Ocean Channel Phase II Groundbreaking Ceremony

    The U.S. Army Corps of Engineers Norfolk District, Port of Virginia and the City of Virginia Beach will host a groundbreaking ceremony for the commencement of the Atlantic Ocean Channel Phase II on Thursday, April 25, 2024, from 10:00 am to 11:00 am.

  • Standardized NEON Organismal Data for Biodiversity Research

    Abstract: Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is limited by a lack of standardized, high-quality, empirical data spanning large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the US and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be accessible and easily integrated into investigators’ workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format available through the ecocomDP R package; provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included. Our workflows are open-source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.

  • Neural Ordinary Differential Equations for Rotorcraft Aerodynamics

    Abstract: High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential Equation (NODE) approach as a machine learning alternative to reduced order models in rotorcraft applications—specifically to predict the pitching moment on a rotor blade section from an initial condition, mach number, chord velocity and normal velocity. The results indicate that NODEs cannot outperform traditional reduced order models, but in some cases they can outperform simple multilayer perceptron networks. Additionally, the mathematical structure provided by NODEs seems to favor time-dependent predictions. We demonstrate how this mathematical structure can be easily modified to tackle more complex problems. The work presented in this report is intended to establish an initial evaluation of the usability of the modified NODE approach for time-dependent modeling of complex dynamics over seen and unseen domains.

  • ERDC’s Ship/Tow Simulator used to assist the Francis Scott Key Bridge Response

    Francis Scott Key Bridge Response Unified Command is utilizing simulation resources from the U.S. Army Engineer Research and Development Center’s (ERDC) Coastal and Hydraulics Laboratory (CHL) to test shipping runs around the Key Bridge accident site.

  • Artificial Intelligence (AI)–Enabled Wargaming Agent Training

    Abstract: Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.

  • IWR Leads USACE Participation in United Nations’ Water and Disasters Panel

    ALEXANDRIA, Va. (April 17, 2024) — The U.S. Army Corps of Engineers (USACE) is a participant in the United Nations' (UN) High-level Experts and Leaders Panel on Water and Disasters (HELP). Established in 2007 at the request of the UN Secretary General’s Advisory Board on Water and Sanitation (UNSGAB), HELP comprises 21 international organizations to address water disaster preparedness and response. The group collaborates closely with the UN Secretariat for the Office for Disaster Reduction (UNDRR). The USACE representative to HELP is the USACE chief of engineers, Lt. Gen. Scott Spellmon.

  • Incorporating environmental flows through the Sustainable Rivers Program to support lake sturgeon spawning continues to prove successful

    The lake sturgeon, an ancient whisker-snouted fish from the Cretaceous period, is tied to present-day conservation efforts on the Mississippi River at the Melvin Price Locks and Dam in West Alton, Missouri. Despite their name, lake sturgeon, also known as “rubbernose or rock” sturgeon, are found in rivers and lakes. Evolving 150 million years ago, long before the evolution of the T-Rex and the other dinosaurs, they have scale-less skin and diamond-shaped plates along their back. Mature lake sturgeon live up to their unique legacy by reaching eight feet in length, weighing more than 200 pounds, and living over 100 years, making them extraordinarily impressive fish. These giants of the fish world are sustained from a diet of snails, crayfish, mussels, and aquatic insects found with barbel sensors and their suction-like toothless mouths.

  • Enabling Understanding of Artificial Intelligence (AI) Agent Wargaming Decisions through Visualizations

    Abstract: The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a maritime scenario where the AI agent credibly competes against blue agents in gameplay. However, a limitation of using DRL for agent training relates to the transparency of how the AI agent makes decisions. If leaders were to rely on AI agents for COA development or analysis, they would want to understand those decisions. In or-der to support increased understanding, researchers engaged with stakeholders to determine visualization requirements and developed initial prototypes for stakeholder feedback in order to support increased understanding of AI-generated decisions and recommendations. This report describes the prototype visualizations developed to support the use case of a mission planner and an AI agent trainer. The prototypes include training results charts, heat map visualizations of agent paths, weight matrix visualizations, and ablation testing graphs.