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DOE OSTI2024

Surrogate construction via weight parameterization of residual neural networks

Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. Here, in this work, we develop a multi-output surrogate form using a class of neural networks (NNs) that employ shortcut connections, namely Residual NNs (ResNets). ResNets are known to regularize the surrogate learning problem and improve the efficiency and accuracy of the resulting surrogate. Inspired by the continuous, Neural ODE analogy, we augment ResNets with weight parameterization strategy with respect to ResNet depth. Weight-parameterized ResNets regularize the NN surrogate learning problem and allow better generalization with a drastically reduced number of learnable parameters. We demonstrate that weight-parameterized ResNets are more accurate and efficient than conventional feed-forward multi-layer perceptron networks. We also compare various options for parameterization of the weights as functions of ResNet depth. We demonstrate the results on both synthetic examples and a large scale earth system model of interest.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2000

Efficient Analysis of Complex Structures

Last various accomplishments achieved during this project are : (1) A Survey of Neural Network (NN) applications using MATLAB NN Toolbox on structural engineering especially on equivalent continuum models (Appendix A). (2) Application of NN and GAs to simulate and synthesize substructures: 1-D and 2-D beam problems (Appendix B). (3) Development of an equivalent plate-model analysis method (EPA) for static and vibration analysis of general trapezoidal built-up wing structures composed of skins, spars and ribs. Calculation of all sorts of test cases and comparison with measurements or FEA results. (Appendix C). (4) Basic work on using second order sensitivities on simulating wing modal response, discussion of sensitivity evaluation approaches, and some results (Appendix D). (5) Establishing a general methodology of simulating the modal responses by direct application of NN and by sensitivity techniques, in a design space composed of a number of design points. Comparison is made through examples using these two methods (Appendix E). (6) Establishing a general methodology of efficient analysis of complex wing structures by indirect application of NN: the NN-aided Equivalent Plate Analysis. Training of the Neural Networks for this purpose in several cases of design spaces, which can be applicable for actual design of complex wings (Appendix F).

Kapania, Rakesh K.↗

NASA NTRS2017

Enabling Future Science and Human Exploration with NASA's Next Generation near Earth and Deep Space Communications and Navigation Architecture

The National Aeronautics and Space Administration (NASA) is studying alternatives for the United States space communications architecture through the 2040 timeframe. This architecture provides communication and navigation services to both human exploration and science missions throughout the solar system. Several of NASA's key space assets are approaching their end of design life and major systems are in need of replacement. The changes envisioned in the relay satellite architecture and capabilities around both Earth and Mars are significant undertakings and occur only once or twice each generation, and therefore is referred to as NASA's next generation space communications architecture. NASA's next generation architecture will benefit from technology and services developed over recent years. These innovations will provide missions with new operations concepts, increased performance, and new business and operating models. Advancements in optical communications will enable high-speed data channels and the use of new and more complex science instruments. Modern multiple beam/multiple access technologies such as those employed on commercial high throughput satellites will enable enhanced capabilities for on-demand service, and with new protocols will help provide Internet-like connectivity for cooperative spacecraft to improve data return and coordinate joint mission objectives. On-board processing with autonomous and cognitive networking will play larger roles to help manage system complexity. Spacecraft and ground systems will coordinate among themselves to establish communications, negotiate link connectivity, and learn to share spectrum to optimize resource allocation. Spacecraft will autonomously navigate, plan trajectories, and handle off-nominal events. NASA intends to leverage the ever-expanding capabilities of the satellite communications industry and foster its continued growth. NASA's technology development will complement and extend commercial capabilities to meet unique space environment requirements and to provide capabilities that are beyond the commercial marketplace. The progress of the communications industry, including the emerging global space internet segment and its planned constellations of 100's of satellites offer additional opportunities for new capability and mission concepts. The opportunities and challenges of a future space architecture require an optimal solution encompassing a global perspective. The concepts and technologies intentionally define an architecture that applies not only to NASA, but to other U.S. government agencies, international space and government agencies, and domestic and international industries to advance the openness, interoperability, and affordability of space communications. Cooperation among the worlds space agencies, their capabilities, standards, operations, and interoperability are key to advancing humankind's understand of the universe and extending human presence into the solar system.

Communications↗

NASA NTRS2017

Enabling Future Science and Human Exploration with NASA's Next Generation Near Earth and Deep Space Communications and Navigation Architecture

The National Aeronautics and Space Administration (NASA) is studying alternatives for the United States space communications architecture through the 2040 timeframe. This architecture provides communication and navigation services to both human exploration and science missions throughout the solar system. Several of NASA's key space assets are approaching their end of design life and major systems are in need of replacement. The changes envisioned in the relay satellite architecture and capabilities around both Earth and Mars are significant undertakings and occur only once or twice each generation, and therefore is referred to as NASA's next generation space communications architecture. NASA's next generation architecture will benefit from technology and services developed over recent years. These innovations will provide missions with new operations concepts, increased performance, and new business and operating models. Advancements in optical communications will enable high-speed data channels and the use of new and more complex science instruments. Modern multiple beam/multiple access technologies such as those employed on commercial high throughput satellites will enable enhanced capabilities for on-demand service, and with new protocols will help provide Internet-like connectivity for cooperative spacecraft to improve data return and coordinate joint mission objectives. On-board processing with autonomous and cognitive networking will play larger roles to help manage system complexity. Spacecraft and ground systems will coordinate among themselves to establish communications, negotiate link connectivity, and learn to share spectrum to optimize resource allocation. Spacecraft will autonomously navigate, plan trajectories, and handle off-nominal events. NASA intends to leverage the ever-expanding capabilities of the satellite communications industry and foster its continued growth. NASA's technology development will complement and extend commercial capabilities to meet unique space environment requirements and to provide capabilities that are beyond the commercial marketplace. The progress of the communications industry, including the emerging global space internet segment and its planned constellations of 100's of satellites offer additional opportunities for new capability and mission concepts. The opportunities and challenges of a future space architecture require an optimal solution encompassing a global perspective. The concepts and technologies intentionally define an architecture that applies not only to NASA, but to other U.S. government agencies, international space and government agencies, and domestic and international industries to advance the openness, interoperability, and affordability of space communications. Cooperation among the worlds space agencies, their capabilities, standards, operations, and interoperability are key to advancing humankinds understand of the universe and extending human presence into the solar system.

deep space↗

DOE OSTI2024

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics–Informed Neural Networks

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Furthermore, the improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2002

Computing the Envelope for Stepwise-Constant Resource Allocations

Computing tight resource-level bounds is a fundamental problem in the construction of flexible plans with resource utilization. In this paper we describe an efficient algorithm that builds a resource envelope, the tightest possible such bound. The algorithm is based on transforming the temporal network of resource consuming and producing events into a flow network with nodes equal to the events and edges equal to the necessary predecessor links between events. A staged maximum flow problem on the network is then used to compute the time of occurrence and the height of each step of the resource envelope profile. Each stage has the same computational complexity of solving a maximum flow problem on the entire flow network. This makes this method computationally feasible and promising for use in the inner loop of flexible-time scheduling algorithms.

Muscettola, Nicola↗

NASA NTRS1990

Rocket engine diagnostics using neural networks

Two problems in applying neural networks to fault detection and identification are (1) the complexity of the sensor data to fault mapping and (2) the lack of sufficient training data. Here, methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Second, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented. These methods were tested on a task of fault detection and identification in the Space Shuttle Main Engine (SSME). Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗

DOE OSTI2025

Unified Universal Control and Coordination of Inverter-Based Resources, and Validation for a PV + Battery Hybrid Plant

As renewable energy deployment grows, hybrid power plants (HPPs) combining photovoltaic (PV) and battery systems must evolve to offer both energy and grid stability services. These systems typically include a mix of grid-following (GFL) and grid-forming (GFM) inverters, presenting unique coordination and control challenges. This Department of Energy–funded project developed and validated a Unified Universal Control and Coordination (UUCC) framework for such PV + battery hybrid plants, enabling seamless and stable operation, including ultrafast black start, autonomous synchronization, and robust frequency and voltage regulation, under different grid conditions. The project significantly advanced the understanding of inverter-based resource (IBR) control by developing and validating three complementary system-level approaches for hybrid GFL/GFM operation: 1. A combined Virtual Resistance (VR)-based GFL and Virtual Oscillator Control (VOC)-based GFM method, where each inverter type is governed by a specialized control strategy. Together, these achieve stable, fast-response coordination, eliminating inrush current and enabling smooth black start and grid synchronization across a wide range of grid strengths. 2. A Deadbeat-based UUCC strategy, which uses discrete-time, switching-cycle-level control for both GFL and GFM inverters. This approach replaces traditional PI/PLL control with a control parameter-free, high-bandwidth framework that supports stable LVRT and instantaneous synchronization under all conditions. 3. A benchmark comparison with Siemens’ commercial GFM microgrid controller, which provided a fast baseline platform. The commercial approach decoupled v & f control was implemented on a commercial microgrid controller.The baseline commercial benchmark helped highlight superior transient response and black start performance offered by the deadbeat and VOC approaches. These technical contributions offer substantial improvements over conventional inverter control schemes, which often rely on slow phase-locked loop (PLL)-based synchronization, require careful control parameters tuning, and prone to unstable in weak grids with GFL inverters and in stiff grid with GFM inverters therefore challenging for hybrid GFL+GFM under all grid conditions. The deadbeat-based UUCC framework enables simpler, faster, and more robust operation of hybrid IBR systems using wide-bandgap (WBG) devices such as SiC power semiconductors. The rapid expansion of hybrid distributed energy resources (DERs), including residential and commercial PV-BESS installations such as Tesla Powerwall, PV with vehicle-to-grid (V2G) capability, and other integrated configurations, presents complex operational challenges for medium-voltage radial distribution feeders. These networks are subject to frequent disturbances such as faults, switching operations, rapid reclosing sequences, and feeder reconfigurations, all of which introduce dynamic stress on IBRs. In addition, planned feeder segmentation and deliberate islanding for resilience will require DERs that can autonomously perform blackstart, establish voltage and frequency references, and resynchronize with the main grid. The advanced deadbeat-based UUCC control and blackstart functionalities developed in this project directly address these requirements, enabling decentralized and autonomous operation of inverter-dominated DERs in distribution systems under a wide range of fault and reconfiguration scenarios. From a public benefit perspective, these innovations enable more reliable and cost-effective integration of renewable energy into distribution networks. The ability to autonomously black start and stabilize grids under varying grid conditions support accelerates recovery from outages and support decentralized resilient energy systems. By reducing system complexity and improving performance, this project lays critical groundwork for future inverter-dominated power grids that are clean, reliable, and accessible to all.

14 SOLAR ENERGY↗

NASA NTRS2001

Computing the Envelope for Stepwise Constant Resource Allocations

Estimating tight resource level is a fundamental problem in the construction of flexible plans with resource utilization. In this paper we describe an efficient algorithm that builds a resource envelope, the tightest possible such bound. The algorithm is based on transforming the temporal network of resource consuming and producing events into a flow network with noises equal to the events and edges equal to the necessary predecessor links between events. The incremental solution of a staged maximum flow problem on the network is then used to compute the time of occurrence and the height of each step of the resource envelope profile. The staged algorithm has the same computational complexity of solving a maximum flow problem on the entire flow network. This makes this method computationally feasible for use in the inner loop of search-based scheduling algorithms.

Muscettola, Nicola↗

DOE OSTI2025

Deep Point Cloud Building Envelope Segmentation (DeeP-CuBES) using Deep Learning

Building Information Modeling (BIM) plays an important role in building design and construction, particularly for achieving energy-efficient retrofits. Building envelope retrofits using panelized prefabricated system, such as those popularized by the Energiesprong program, need accurate as-built dimensions of facade features (windows, doors, etc.) to achieve the desired thermal and air tightness. Traditionally, building surveying is done manually, resulting in a time-consuming and labor-intensive process. Recently, 3D point clouds from terrestrial LiDAR have been used to automate the generation of as-built dimensions of existing buildings. However, automated BIM using LiDAR relies on solving the point cloud semantic segmentation (PCSS) problem. In this work, we propose a robust pipeline for solving the PCSS problem using deep neural networks, focusing on overcoming challenges posed by imbalanced datasets and complex architectural features. We introduce the first high-density, labeled, and validated building envelope point cloud dataset derived from multiple building scans, specifically curated to tackle challenges in facade-level segmentation. Results from the trained neural networks show that advanced attention-based architectures and incorporating radiometry (light intensity and RGB) features significantly boost segmentation accuracy for windows and doors.

Selvakumar, Balaji [ORNL]↗

NASA NTRS2022

Differential Ranging Experiments with NASA’s High-Gain Deep Space Network Antennas

Radar experiments are currently under way at the Goldstone Deep Space Communications Complex (GDSCC) to determine the range of cis-lunar objects using a single high-gain 34m antenna transmitting 20 kW at X-band wavelengths, and two 34m receivers located at the GDSCC. The 34m antennas of the Deep Space Network (DSN) have effective fields-of-view of approximately 1 milli-radians, hence predetermined pointing predicts with accuracies on the order of 1 milli-degree are required. The extremely accurate 1 nano-second timing distribution of the DSN network enables sub-meter level ranging in the radial direction, by measuring delays in phase-modulated pseudonoise (PN) echoes reflected from the target. In this paper we demonstrate experimental verification of the proposed techniques by post-processing radar reflections obtained from a decommissioned geo-synchronous satellite. The characteristics of correlation-based delay measurements obtained simultaneously with two different receiving antennas are described. These are the fundamental quantities used to form the “time difference of arrival” (TDOA) observables commonly used in GPS positioning algorithms, and the results interpreted in the context of hyperbolic position location that could be used in the future to monitor spacecraft in cis-lunar space.

Cheung, Kar-Ming↗

DOE OSTI2025

Segmental Dynamics and Vitrification in Associating Copolymer Melts: Role of Cluster Formation, Microdomains, and Cross-Linking

A microscopic statistical mechanical theory of the structure, self-assembly, and activated segmental relaxation is employed to study associating copolymer melts with high attractive sticker fractions, local clustering, and disordered microphase ordering. The stickers are dynamically pinned in a manner that does not affect equilibrium structure which mimics the much slower physical bond breaking process or postassembly cross-linking of sticky monomers. Local sticker clustering and microdomain spatial correlations significantly modify the activated relaxation of nonstickers and glass transition temperature, T g . A re-entrant glass-melting feature is predicted as sticker attraction strength is initially increased corresponding to a speed up of segmental relaxation, and hence reduction of T g relative to the cross-linked homopolymer network. A mechanistic analysis reveals three competing effects: a purely kinetic slowing down of nonstickers down due to cross-linking, disordering of the nonsticker local cage and weakening of effective forces they experience due to sticker physical clustering, and a longer range impact of microdomain scale correlations that results in nonmonotonic dynamical effects. At high enough attraction strength, a qualitative change emerges corresponding to a sticker fraction dependent elevation of T g , which eventually surpasses that of the cross-linked homopolymer network. Dynamically, the new physics arises from a complex evolution of the amplitude of the collective elastic field that dresses the large amplitude mobile segment hopping within a coupled local-nonlocal description of the alpha relaxation. Furthermore, the results are qualitatively consistent with recent experiments on associating PDMS and PPG telechelics of fixed sticker fraction but with chemically different end groups of variable attraction strengths. Finally, possible tests using simulation and the influence of material or model specific interaction potentials and other real world complications are discussed.

Copolymers↗

DOE OSTI2025

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Carruthers, David N↗

DOE OSTI2024

Ultrafast jet classification at the HL-LHC

Abstract Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that O ( 100 ) ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm

This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Modeling and Calibration of Supplier Selection Problem in Freight Agent-Based Simulations

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and a supplier-rating model adapted from existing literature. The model’s core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas—Atlanta, Chicago, Dallas–Fort Worth, and Los Angeles—the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world decision making, providing a robust tool for policymakers and planners to evaluate targeted interventions, assess infrastructure investments, and enhance supply chain resilience in the face of disruptions.

Ismael, Abdelrahman (ORCID:0000000303712110)↗

DOE OSTI2026

Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) (Final Technical Report)

The Stakeholder-guided holistic, Adaptive Framework for enhancing community Energy Resilience (SAFER) project advances resilience science and engineering by addressing challenges in rural Kansas communities where aging infrastructure, extreme weather, and socioeconomic disparities heighten vulnerability to energy disruptions. Traditional approaches often focus on technical performance while overlooking community concerns and priorities. SAFER responds by integrating community perspectives with advanced analytical frameworks to create a holistic model for measuring and improving resilience. Project objectives included developing novel resilience metrics, advancing modeling frameworks that capture interdependencies across infrastructures, and embedding community-centric indicators directly into planning processes for distributed energy resources. The key technical innovations included the creation of self-organizing map (SOM)-based indices for objective resilience quantification, hetero-functional graph theory (HFGT) models linking power, water, transportation, and community assets, and graph neural network (GNN) tools for identifying critical nodes in complex systems. Community-centric energy planning was demonstrated through optimal siting and sizing of (photovoltaic) PV and battery storage, ensuring resilience enhancements also addressed energy burden and energy insecurity. SAFER engaged community partners in Dodge City and Ford County through surveys, focus groups, and workshops, generating more than 600 responses that established baseline measures of energy burden, financial insecurity, and willingness-to-pay to avoid outages. This data, organized in terms of a community capitals framework, informed the development of weighted reliability indices that better reflect community costs than traditional utility metrics. SAFER’s GNN-based critical node identification framework identified expert-labelled critical nodes with over 99% accuracy, while also uncovering additional functionalities essential for proactive resilience planning. The project’s models demonstrated that optimal PV and storage deployment could improve resilience indices by over 11 percent, with dispatch strategies further enhancing outcomes, confirming both the technical effectiveness and economic feasibility of these approaches. Through its combined emphasis on rigorous modeling, community-focused planning, and community engagement, SAFER advances the state of resilience research while delivering direct benefits to rural communities. The project provides tools, guidelines, and resilience heatmaps that help utilities, local governments, and residents better anticipate disruptions, prioritize investments, and strengthen the capacity to withstand and recover from energy-related hazards. Furthermore, the developed HFG and GNN frameworks are designed for transferability, allowing them to be adapted for resilience planning in other communities with minimal retraining. This inductive learning capability provides a scalable pathway to extend the SAFER project’s impact. Thus, creating a foundation for a nationally applicable model of infrastructure resilience. Additionally, the HFG can also be extended to include other FEMA community lifelines.

14 SOLAR ENERGY↗

DOE OSTI2024

Decoding FGF/FGFR Signaling: Insights into Biological Functions and Disease Relevance

Fibroblast Growth Factors (FGFs) and their cognate receptors, FGFRs, play pivotal roles in a plethora of biological processes, including cell proliferation, differentiation, tissue repair, and metabolic homeostasis. This review provides a comprehensive overview of FGF-FGFR signaling pathways while highlighting their complex regulatory mechanisms and interconnections with other signaling networks. Further, we briefly discuss the FGFs involvement in developmental, metabolic, and housekeeping functions. By complementing current knowledge and emerging research, this review aims to enhance the understanding of FGF-FGFR-mediated signaling and its implications for health and disease, which will be crucial for therapeutic development against FGF-related pathological conditions.

Edirisinghe, Oshadi↗