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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Optimizing Altitude Sampling and Sensitivity with the Goldstone Orbital Debris Radar

The NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar (Goldstone) since 1993 to characterize orbital debris (OD) in low Earth orbit too small to be tracked by the U.S. Space Surveillance Network. Operated by NASA’s Jet Propulsion Laboratory, Goldstone can measure OD as small as 3 mm at 1000 km altitude and lower. Goldstone is a bistatic radar that for 25 years used Deep Space Station (DSS)-14 as a transmitter and DSS-15 as a receiver. In early 2018, DSS-15 was decommissioned and replaced with DSS-25 (and occasionally DSS-26) of the Deep Space Network Apollo Cluster. The increased baseline between DSS-14 and DSS-25 significantly reduced the instantaneous altitude coverage of the bistatic beam overlap. Initial measurements in 2018 were focused around 800 km, which has approximately the highest flux of sub-centimeter debris. In 2019, DSS-14 was offline for maintenance, and the ODPO designed an annual survey observation plan to efficiently sample altitudes from 700 km to 1000 km, since many NASA satellites fly in this range. This paper discusses the observation plan, including the development of the pointings, a refinement of the altitudes of interest, and an analysis of the effects of random pointing errors on beam overlap. Additionally, results from measurements taken in 2020 and 2021 are presented, showing that not only is the observation plan effective at sampling 700 km to 1000 km altitude, but it is also producing the most sensitive terrestrial radar measurements at these altitudes to date.

James Murray↗
NASA NTRS1999

Development and Performance of the ACTS High Speed VSAT

The Advanced Communication Technology Satellite (ACTS), developed by the U.S. National Aeronautics and Space Administration (NASA) has demonstrated the breakthrough technologies of Ka-band, spot beam antennas, and on-board processing. These technologies have enabled the development of very small aperture terminals (VSAT) and ultra-small aperture terminals (USAT) which have capabilities greater than were previously possible with conventional satellite technologies. However, the ACTS baseband processor (BBP) is designed using a time division multiple access (TDMA) scheme, which requires each earth station using the BBP to transmit data at a burst rate which is much higher than the user throughput data rate. This tends to mitigate the advantage of the new technologies by requiring a larger earth station antenna and/or a higher-powered uplink amplifier than would be necessary for a continuous transmission at the user data rate. Conversely, the user data rate is much less than the rate that can be supported by the antenna size and amplifier. For example, the ACTS TI VSAT operates at a burst rate of 27.5 Mbps, but the maximum user data rate is 1.792 Mbps. The throughput efficiency is slightly more than 6.5%. For an operational network, this level of overhead will greatly increase the cost of the user earth stations, and that increased cost must be repeated thousands of times, which may ultimately reduce the market for such a system. The ACTS High Speed VSAT (HS VSAT) is an effort to experimentally demonstrate the maximum user throughput data rate which can be achieved using the technologies developed and implemented on ACTS. Specifically, this was done by operating the system uplinks as frequency division multiple access (FDMA), essentially assigning all available TDMA time slots to a single user on each of two uplink frequencies. Preliminary results show that using a 1.2-m antenna in this mode, the HS VSAT can achieve between 22 and 24 Mbps out of the 27.5 Mbps burst rate, for a throughput efficiency of 80-88%. This paper describes the modifications made to the TI VSAT to enable it to operate at high speed, including hardware considerations, interface modifications, and software modifications. In addition, it describes the results of NASA HS VSAT experiments, continuing work on an improved user interface, and plans for future experiments.

Quintana, J.↗
DOE OSTI2020

Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints

This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

active distribution networks↗
DOE OSTI2026

SPARTAN and IMPROVE Comparison Experiment (SPICE) Interim Campaign Report

SPICE (the SPARTAN and IMPROVE Comparison Experiment) aims to obtain and quantify comparisons between aerosol PM 2.5 mass concentration measurements from the University of Oklahoma (OU) Surface Particulate Matter Network (SPARTAN) station and the U.S. Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) station hosted by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility at ARM’s Southern Great Plains (SGP) observatory in Oklahoma. Aerosols—both natural and anthropogenic—affect human populations in multiple ways. Much of ARM’s research focuses on how aerosols influence weather and climate through their optical and radiative effects, as well as their impacts on clouds and precipitation. However, aerosols also pose direct risks to humans and other organisms through inhalation, with the severity of health impacts depending on particle size, chemical composition, and duration of exposure. The IMPROVE network was established by the U.S. EPA to monitor air quality, including visible clarity as well as total and chemically speciated aerosol mass concentrations. The ARM SGP site hosts the IMPROVE SOGP station. Separately, the SPARTAN network operates a globally distributed set of stations similar to IMPROVE but with an emphasis on remote deployment and semi-autonomous operation for use beyond the borders of the United States (IMPROVE only operates within the U.S.). The University of Oklahoma operates a SPARTAN station. To establish confidence in the OU SPARTAN instrumentation and measurement protocol relative to the EPA-certified IMPROVE station, the OU SPARTAN station is currently deployed at SGP in close proximity to the IMPROVE SOGP station. The SPICE campaign was envisioned as a contiguous calendar-year effort for 2025 to capture seasonal variation in mass loading as well as composition. However, independent of the SPICE campaign, the SPARTAN network adopted a new filter construction part-way through the year, interrupting our contiguous data set. Thus, to obtain a contiguous data set with a uniform consistent configuration, SPICE desires an extension through 2026.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Generalization of Deep-Learning Models for Classification of Local Distance Earthquakes and Explosions across Various Geologic Settings

Although accurately classifying signals from earthquakes and explosions at local distance (<250 km) remains an important task for seismic network operations, the growing volume of available seismic data presents a challenge for analysts using traditional source discrimination techniques. In recent years, deep-learning models have proven effective at discriminating between low-magnitude earthquakes and explosions measured at local distances, but it is not clear how well these models are capable of generalizing across different geological settings. To address the issue of generalization between regions, we train deep-learning models (convolutional neural networks [CNNs]) on time–frequency representations (scalograms) of three-component earthquake and explosion signals from eight different regions in the continental United States. We explore scenarios where models are trained on data from all regions, individual regions, or all but one region. We find that although CNN models trained on individual regions do not necessarily generalize well across different settings, models trained on multiple regions that include diverse path coverage generalize to new regions, with station-level accuracy of up to 90% or more for data sets from unseen regions. In general, CNN-based discrimination models significantly outperform models based on uncorrected P/S ratio (measured in the 10–18 Hz frequency band), even when CNN models are tested on data from entirely unseen regions.

58 GEOSCIENCES↗
NASA NTRS2011

Dual-Polarimetric Radar-Based Tornado Debris Paths Associated with EF-4 and EF-5 Tornadoes over Northern Alabama During the Historic Outbreak of 27 April 2011

An historic tornado and severe weather outbreak devastated much of the southeastern United States between 25 and 28 April 2011. On 27 April 2011, northern Alabama was particularly hard hit by a large number of tornadoes, including several that reached EF-4 and EF-5 on the Enhanced Fujita damage scale. In northern Alabama alone, there were approximately 100 fatalities and hundreds of more people who were injured or lost their homes during the havoc caused by these violent tornadic storms. Two long-track and violent (EF-4 and EF-5) tornadoes occurred within range of the University of Alabama in Huntsville (UAHuntsville) Advanced Radar for Meteorological and Operational Research (ARMOR, C-band dual-polarimetric). A unique capability of dual-polarimetric radar is the near-real time identification of lofted debris associated with ongoing tornadoes on the ground. The focus of this paper is to analyze the dual-polarimetric radar-inferred tornado debris signatures and identify the associated debris paths of the long-track EF-4 and EF-5 tornadoes near ARMOR. The relative locations of the debris and damage paths for each tornado will be ascertained by careful comparison of the ARMOR analysis with NASA MODIS (Moderate Resolution Imaging Spectroradiometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite imagery of the tornado damage scenes and the National Weather Service tornado damage surveys. With the ongoing upgrade of the WSR-88D (Weather Surveillance Radar - 1988 Doppler) operational network to dual-polarimetry and a similar process having already taken place or ongoing for many private sector radars, dual-polarimetric radar signatures of tornado debris promise the potential to assist in the situational awareness of government and private sector forecasters and emergency managers during tornadic events. As such, a companion abstract (Schultz et al.) also submitted to this conference explores "The use of dual-polarimetric tornadic debris signatures in an operational setting."

Carey, Lawrence D.↗
NASA NTRS2012

Dual-Polarimetric Radar-Based Tornado Debris Paths Associated with EF-4 and EF-5 Tornadoes over Northern Alabama During the Historic Outbreak of 27 April 2011

An historic tornado and severe weather outbreak devastated much of the southeastern United States between 25 and 28 April 2011. On 27 April 2011, northern Alabama was particularly hard hit by a large number of tornadoes, including several that reached EF-4 and EF-5 on the Enhanced Fujita damage scale. In northern Alabama alone, there were approximately 100 fatalities and hundreds of more people who were injured or lost their homes during the havoc caused by these violent tornadic storms. Two long-track and violent (EF-4 and EF-5) tornadoes occurred within range of the University of Alabama in Huntsville (UAHuntsville) Advanced Radar for Meteorological and Operational Research (ARMOR, C-band dual-polarimetric). A unique capability of dual-polarimetric radar is the near-real time identification of lofted debris associated with ongoing tornadoes on the ground. The focus of this paper is to analyze the dual-polarimetric radar-inferred tornado debris signatures and identify the associated debris paths of the long-track EF-4 and EF-5 tornadoes near ARMOR. The relative locations of the debris and damage paths for each tornado will be ascertained by careful comparison of the ARMOR analysis with NASA MODIS (Moderate Resolution Imaging Spectroradiometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite imagery of the tornado damage scenes and the National Weather Service tornado damage surveys. With the ongoing upgrade of the WSR-88D (Weather Surveillance Radar 1988 Doppler) operational network to dual-polarimetry and a similar process having already taken place or ongoing for many private sector radars, dual-polarimetric radar signatures of tornado debris promise the potential to assist in the situational awareness of government and private sector forecasters and emergency managers during tornadic events. As such, a companion abstract (Schultz et al.) also submitted to this conference explores The use of dual-polarimetric tornadic debris signatures in an operational setting.

Carey, Lawrence D.↗
DOE OSTI2022

Data recovery via covert cognizance for unattended operational resilience

One of the important premises of unattended operation, a highly promoted characteristic of fission batteries and advanced microreactors, is the ability to automate the analysis of sensors data used in support of operational monitoring and control. Here, to meet this vision, this work proposes a new monitoring and data recovery paradigm to ensure resilience against data corruption which may be the result of malicious intrusion into the reactor operational network. This is paramount to ensure 100% availability under contingency scenarios such as cyberattacks. In support of this vision, earlier work has presented the concept of covert cognizance and demonstrated its mathematical ability to identify and embed cognizance parameters under the noise-dominated null space of the sensors data. This work extends this concept and applies it in real-time to demonstrate three key characteristics: zero-impact, zero-observability, and data recovery, where the first characteristic is to ensure no impact on operation, the second is immunity to discovery by pattern recognition techniques, and the third is to allow recovery of corrupt or falsified data. Recognizing that fission batteries are designed to operate under steady state most of the time, we elect to employ a small modular reactor model under transient operational conditions to demonstrate the operational resilience enabled by the covert cognizance paradigm. Specifically, the PI controller is augmented with the covert cognizance modules to develop self-awareness and enable automatic data recovery. The developed modules are expected to be equally applicable to a wide range of advanced reactor technologies relying on full or partial unattended control.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Secure mmWave Spectrum Sharing with Autonomous Beam Scheduling for 5G and Beyond

Spectrum Sharing (SS) has seen a renewed set of initiatives in 5G with the availability of shared and unlicensed spectrum bands that can be used by multiple cellular service providers and private cellular networks. Beam based transmission, instead of the traditional sector based transmission in conjunction with the spectrum agility of the 5G New Radio (NR) has brought new opportunities to optimized sharing of spectrum. Currently in the U.S., a centralized Spectrum Access Server (SAS) is used to co-ordinate spectrum sharing among networks sharing the same spectrum band. However, SAS becomes a focal point for security attacks and a performance bottleneck. In addition, SAS relies on an Environmental Sensor Network (ESN), separate from the 5G network. Without trusted spectral occupancy information, false reporting of spectrum sensing data can create sub-optimal and unfair spectrum usage. This paper summarizes our recent research findings in using a decentralized scheme for multiple networks to securely share spectrum with autonomous beam scheduling : 1) A new stochastic network framework based on Lyapunov Optimization approach is developed to optimize scheduling at the base stations; 2) Game theoretic (GT) approach is used to formulate the distributed scheduler; 3) Another distributed scheduler with Q-learning is presented that utilizes the Reinforcement Learning (RL) approach; 4) The performance and convergence rate of these distributed solutions to use shared and unlicensed spectrum are compared with existing solutions. Conditions under which the performance of these schedulers approach the theoretical upper bound, which is the performance possible with no interference among the operators sharing the spectrum, are presented; 5) The ability of a base station to use its own user equipment as sensors, for optimal spectrum sharing with base stations in other operator networks, is demonstrated to be an effective approach.

5G↗
NASA NTRS1972

Description of electrical networks using wiring operators.

A scheme is presented for describing the interconnection of elements and subnetworks into networks by means of 'wiring operators.' This scheme is algebraic and is intended for conversing with computers. It is especially suitable for two-ports such as amplifiers, filters, microwave networks, and in general, transmission-type networks with an input and an output. The algebra is direct, convenient, and versatile. Each of the wiring operators corresponds to a specific set of calculations on the parameters of the networks involved.

Penfield, P., Jr.↗
NASA NTRS2020

Utilizing Small Telescopes Operated by Citizen Scientists for Transiting Exoplanet Follow-up

Due to the efforts by numerous ground-based surveys and NASA's Kepler and Transiting Exoplanet Survey Satellite (TESS), there will be hundreds, if not thousands, of transiting exoplanets ideal for atmospheric characterization via spectroscopy with large platforms such as James Webb Space Telescope and ARIEL. However their next predicted mid-transit time could become so increasingly uncertain over time that significant overhead would be required to ensure the detection of the entire transit. As a result, follow-up observations to characterize these exoplanetary atmospheres would require less-efficient use of an observatory's time—which is an issue for large platforms where minimizing observing overheads is a necessity. Here we demonstrate the power of citizen scientists operating smaller observatories (≤1 m) to keep ephemerides "fresh," defined here as when the 1σ uncertainty in the mid-transit time is less than half the transit duration. We advocate for the creation of a community-wide effort to perform ephemeris maintenance on transiting exoplanets by citizen scientists. Such observations can be conducted with even a 6 inch telescope, which has the potential to save up to ∼10,000 days for a 1000-planet survey. Based on a preliminary analysis of 14 transits from a single 6 inch MicroObservatory telescope, we empirically estimate the ability of small telescopes to benefit the community. Observations with a small-telescope network operated by citizen scientists are capable of resolving stellar blends to within 5''/pixel, can follow-up long period transits in short-baseline TESS fields, monitor epoch-to-epoch stellar variability at a precision 0.67% ± 0.12% for a 11.3 V-mag star, and search for new planets or constrain the masses of known planets with transit timing variations greater than two minutes.

Robert T. Zellem↗
DOE OSTI2024

The Urban Deployment Model: A Toolset for the Simulation and Performance Characterization of Radiation Detector Deployments in Urban Environments

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1977

Absolute flux density calibrations of radio sources: 2.3 GHz

A detailed description of a NASA/JPL Deep Space Network program to improve S-band gain calibrations of large aperture antennas is reported. The program is considered unique in at least three ways; first, absolute gain calibrations of high quality suppressed-sidelobe dual mode horns first provide a high accuracy foundation to the foundation to the program. Second, a very careful transfer calibration technique using an artificial far-field coherent-wave source was used to accurately obtain the gain of one large (26 m) aperture. Third, using the calibrated large aperture directly, the absolute flux density of five selected galactic and extragalactic natural radio sources was determined with an absolute accuracy better than 2 percent, now quoted at the familiar 1 sigma confidence level. The follow-on considerations to apply these results to an operational network of ground antennas are discussed. It is concluded that absolute gain accuracies within + or - 0.30 to 0.40 db are possible, depending primarily on the repeatability (scatter) in the field data from Deep Space Network user stations.

Freiley, A. J.↗
NASA NTRS2006

Characterization of Days Based On Analysis of National Airspace System Performance Metrics

Days of operations in the National Airspace System can be described in term of traffic demand, runway conditions, equipment outages, and surface and enroute weather conditions. These causes manifest themselves in terms of departure delays, arrival delays, enroute delays and traffic flow management delays, Traffic flow management initiatives such as, ground stops, ground delay programs, miles-in-trail restrictions, rerouting and airborne holding are imposed to balance the air traffic demand with respect to the available capacity, In order to maintain operational efficiency of the National Airspace System, the Federal Aviation Administration (FAA) maintains delay sad other statistics in the Air Traffic Operations Network (OPSNET) and the Aviation System Performance Metrics (ASPM) databases. OPSNET data includes reportable delays of fifteen minutes ox more experienced by Instrument Flight Rule (IFR) flights. Numbers of aircraft affected by departure delays, enroute delays, arrival delays and traffic flow delays are recorded in the OPSNET data. ASPM data consist of number of actual departures, number of canceled departures, percentage of on time departures, percentage of on time gate arrivals, taxi-out delays. taxi-in delays, gate delays, arrival delays and block delays. Surface conditions at the major U.S. airports are classified in terms of Instrument Meteorological Condition (IMC) and Visual Meteorological Condition (VMC) as a function of the time of the day in the ASPM data. The main objective of this paper is to use OPSNET and ASPM data to classify the days in the datasets into few distinct groups, where each group is separated from the other groups in terms of a distance metric. The motivations for classifying the days are two-fold, 1) to enable selection of days of traffic with particular operational characteristics for concept evaluation using system-wide simulation systems such as the National Aeronautics and Space Administration's Airspace Concepts Evaluation Tool (ACES) and 2) to enable evaluation of a given day with respect to the characteristics of the classified groups. The first part of the paper is devoted to the analysis of major trends seen in the OPSNET and ASPM data. The second part of the paper is devoted to describing features or measures derived from the OPSNET and ASPM data that are suitable for characterizing days, and the classification algorithm used for grouping the days. Finally, the method for evaluating the characteristics of a given day with respect to the properties of the groups is described.

Chatterji, Gano B.↗
NASA NTRS1998

Autonomous Satellite Command and Control Through the World Wide Web

The Automated Space System Experimental Testbed (ASSET) system is a simple yet comprehensive real-world operations network being developed. Phase 3 of the ASSET Project was January-December 1997 and is the subject of this report. This phase permitted SSDL and its project partners to expand the ASSET system in a variety of ways. These added capabilities included the advancement of ground station capabilities, the adaptation of spacecraft on-board software, and the expansion of capabilities of the ASSET management algorithms. Specific goals of Phase 3 were: (1) Extend Web-based goal-level commanding for both the payload PI and the spacecraft engineer. (2) Support prioritized handling of multiple (PIs) Principle Investigators as well as associated payload experimenters. (3) Expand the number and types of experiments supported by the ASSET system and its associated spacecraft. (4) Implement more advanced resource management, modeling and fault management capabilities that integrate the space and ground segments of the space system hardware. (5) Implement a beacon monitoring test. (6) Implement an experimental blackboard controller for space system management. (7) Further define typical ground station developments required for Internet-based remote control and for full system automation of the PI-to-spacecraft link. Each of those goals are examined. Significant sections of this report were also published as a conference paper. Several publications produced in support of this grant are included as attachments. Titles include: 1) Experimental Initiatives in Space System Operations; 2) The ASSET Client Interface: Balancing High Level Specification with Low Level Control; 3) Specifying Spacecraft Operations At The Product/Service Level; 4) The Design of a Highly Configurable, Reusable Operating System for Testbed Satellites; 5) Automated Health Operations For The Sapphire Spacecraft; 6) Engineering Data Summaries for Space Missions; and 7) Experiments In Automated Health Assessment And Notification For The Sapphire Microsatellite.

Cantwell, Brian↗
DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗
DOE OSTI2024

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

The deep operator network (DeepONet) has shown remarkable potential in solving partial differential equations (PDEs) by mapping between infinite-dimensional function spaces using labeled datasets. However, in scenarios lacking labeled data, the physics-informed DeepONet (PI-DeepONet) approach, which utilizes the residual loss of the governing PDE to optimize the network parameters, faces significant computational challenges, particularly due to the curse of dimensionality. This limitation has hindered its application to high-dimensional problems, making even standard 3D spatial with 1D temporal problems computationally prohibitive. Additionally, the computational requirement increases exponentially with the discretization density of the domain. Here, to address these challenges and enhance scalability for high-dimensional PDEs, we introduce the Separable physics-informed DeepONet (Sep-PI-DeepONet). This framework employs a factorization technique, utilizing sub-networks for individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix required for gradient computations. By incorporating forward-mode automatic differentiation (AD), we further optimize computational efficiency, achieving linear scaling of computational cost with discretization density and dimensionality, making our approach highly suitable for high-dimensional PDEs. We demonstrate the effectiveness of Sep-PI-DeepONet through three benchmark PDE models: the viscous Burgers’ equation, Biot’s consolidation theory, and a parameterized heat equation. Our framework maintains accuracy comparable to the conventional PI-DeepONet while reducing training time by two orders of magnitude. Notably, for the heat equation solved as a 4D problem, the conventional PI-DeepONet was computationally infeasible (estimated 289.35 h), while the Sep-PI-DeepONet completed training in just 2.5 h. These results underscore the potential of Sep-PI-DeepONet in efficiently solving complex, high-dimensional PDEs, marking a significant advancement in physics-informed machine learning.

Neural operator↗
DOE OSTI2022

Learning to identify semi-visible jets

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-p T jet constituents.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗