Results for “Deep Direct Use” — Page 27

DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Learning thermodynamic master equations for open quantum systems

The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.

Mathematics and Computing↗

DOE OSTI2024

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3–20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.

42 ENGINEERING↗

NASA NTRS1986

Dynamics of the North Sea pole tide reconsidered

The anomalous tide in the North Sea, driven by the Chandler wobble, is here re-examined. A previously published solution in which these 'pole tide' observations were explained as a forced co-oscillation from the deep water tide is shown to be untenable, because it is very unlikely that the deep ocean tide is anything but equilibrium. Using the same physics as in the previous solution (viscous, linear, quasi-geostrophic dynamics), it is now suggested that the only plausible explanation is that there is a coincidental resonance between the long-wavelength branch topographic Rossby wave of the North Sea and the direct driving by the pole tide potential. This resonance is not a basin-resonance, but that of a free wave driven by a travelling forcing function. Simplified models reproduce the observations in qualitative fashion. If this explanation is the correct one, then the Arctic seas north of Asia and North America appear to be likely candidates as other regions where there may be a large pole tide response and, hence, a locus of wobble dissipation. Given the difficulties of observation, the long times required, and the general instability of the dissipation calculation with the simplified analytical model, probably a numerical model will be required for further progress.

Wunsch, Carl↗

NASA NTRS2008

NASA and Superalloys: A Customer, a Participant, and a Referee

NASA has had a long history of research and development in the field of superalloys. These efforts have continued today, where the latest advancements in turbine disk and blade technologies are being developed Although NASA does support military flight systems, it s predominant role is in supporting civilian air transportation systems, and thus has goals for improving fuel efficiency, emissions, noise, and safety of today s aircraft. NASA has traditionally served several distinct but complimentary roles as participants in multi-disciplinary research teams, as customers who fund research and development efforts at industry and universities, and as referees who can address broad issues that affect the entire aeronautics community. Because of our longer range viewpoint, we can take on higher risk, higher reward research topics. NASA can also serve as an intermediary between the basic research performed primarily at universities and the development efforts emphasized by industry. By interacting with individual companies, NASA can identify areas of general interest and problems common to a large portion of the aeronautics community, and devise programs aimed at solving these problems. In space missions, NASA is a direct customer responsible for developing vehicles. In the case of the Space Shuttle, NASA has worked with various contractors to design and build numerous components out of superalloys. Another fascinating area for the use of superalloys is in power systems for long life applications in space. Potential missions include providing electric power for deep space missions, surface rovers, including lunar and Mars, and stationary power generators on the lunar surface.

Nathal, Michael V.↗

NASA NTRS2008

NASA and Superalloys: A Customer, a Participant, and a Referee

NASA has had a long history of research and development in the field of superalloys. These efforts have continued today, where the latest advancements in turbine disk and blade technologies are being developed. Although NASA does support military flight systems, its predominant role is in supporting civilian air transportation systems, and thus has goals for improving fuel efficiency, emissions, noise, and safety of today s aircraft. NASA has traditionally served several distinct but complimentary roles as participants in multi-disciplinary research teams, as customers who fund research and development efforts at industry and universities, and as referees who can address broad issues that affect the entire aeronautics community. Because of our longer range viewpoint, we can take on higher risk, higher reward research topics. NASA can also serve as an intermediary between the basic research performed primarily at universities and the development efforts emphasized by industry. By interacting with individual companies, NASA can identify areas of general interest and problems common to a large portion of the aeronautics community, and devise programs aimed at solving these problems. In space missions, NASA is a direct customer responsible for developing vehicles. In the case of the Space Shuttle, NASA has worked with various contractors to design and build numerous components out of superalloys. Another fascinating area for the use of superalloys is in power systems for long life applications in space. Potential missions include providing electric power for deep space missions, surface rovers, including lunar and Mars, and stationary power generators on the lunar surface.

Nathal, Michael V.↗

NASA NTRS2012

Forming Human-Robot Teams Across Time and Space

NASA pushes telerobotics to distances that span the Solar System. At this scale, time of flight for communication is limited by the speed of light, inducing long time delays, narrow bandwidth and the real risk of data disruption. NASA also supports missions where humans are in direct contact with robots during extravehicular activity (EVA), giving a range of zero to hundreds of millions of miles for NASA s definition of "tele". . Another temporal variable is mission phasing. NASA missions are now being considered that combine early robotic phases with later human arrival, then transition back to robot only operations. Robots can preposition, scout, sample or construct in advance of human teammates, transition to assistant roles when the crew are present, and then become care-takers when the crew returns to Earth. This paper will describe advances in robot safety and command interaction approaches developed to form effective human-robot teams, overcoming challenges of time delay and adapting as the team transitions from robot only to robots and crew. The work is predicated on the idea that when robots are alone in space, they are still part of a human-robot team acting as surrogates for people back on Earth or in other distant locations. Software, interaction modes and control methods will be described that can operate robots in all these conditions. A novel control mode for operating robots across time delay was developed using a graphical simulation on the human side of the communication, allowing a remote supervisor to drive and command a robot in simulation with no time delay, then monitor progress of the actual robot as data returns from the round trip to and from the robot. Since the robot must be responsible for safety out to at least the round trip time period, the authors developed a multi layer safety system able to detect and protect the robot and people in its workspace. This safety system is also running when humans are in direct contact with the robot, so it involves both internal fault detection as well as force sensing for unintended external contacts. The designs for the supervisory command mode and the redundant safety system will be described. Specific implementations were developed and test results will be reported. Experiments were conducted using terrestrial analogs for deep space missions, where time delays were artificially added to emulate the longer distances found in space.

Hambuchen, Kimberly↗

NASA NTRS2016

Evaluation of Different MODIS AOD Retrieval Algorithms for PM (sub 2.5) Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP (Minority University Research and Education Project) Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns PM (sub 2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM (sub 2.5) varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target versus Deep Blue), Collections (5.1 versus 6) and spatial resolutions (10-kilometers versus 3-kilometers) for cities in the Western, Midwestern and Southeastern U.S. We developed and validated PM (sub 2.5) prediction models using remotely-sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM (sub 2.5) models, especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Our study re-establishes the connection between PM (sub 2.5) and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM (sub 2.5) data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM (sub 2.5).

Environmental Public Health↗

NASA NTRS2018

A Plasma Aerocapture and Entry System for Manned Missions and Planetary Deep Space Orbiters

A rapid mission to Mars requires a large change in vehicle velocity upon arrival to establish a stable orbit. This demand is even greater for a Neptune science, requiring many kilometers per second of V. It is clear from past mission studies that a manned Mars mission and deep space planetary orbiters require aerobraking and aerocapture which use aerodynamic drag forces to slow the spacecraft. Aerocapture would enable long term studies of the outer planets and moons that would not be possible with existing braking methodologies. While the ability to utilize these atmospheres to slow down and capture spacecraft would dramatically reduce the cost, launch mass, and travel time, currently planned approaches require significant additional spacecraft mass and risk as the spacecraft must descend deep into the planetary atmosphere in order to produce significant drag on a relatively small aeroshell. The plasma based Magnetoshell being developed in this program holds the potential to perform the desired braking with significantly increased drag and control while dramatically reducing mass. Most importantly, this technology significantly lowers the risk involved with aerocapture thereby making manned planetary missions possible. The fundamental physics of the Magnetoshell is based on demonstrated experimental results. Successful implementation will dramatically decrease radiation exposures, mission risk, launch cost, and launch mass. Implementation of aerobraking by employing a solid deflector or aeroshell as a method for orbit insertion and circularization has been successfully demonstrated in the past, resulting in launch mass savings greater than 50%. In order to reduce the effect of frictional heating and dynamic pressure on the typically fragile aeroshell, or worse solar panels, the braking must be distributed over many orbital passes at a high altitude in the less dense regions of the atmosphere. It can thus take several months for a meter-scale, 1000 kg, aeroshell to execute the many elliptic orbital passes through the atmosphere to achieve the required V. This rather slow method of braking not only reduces frictional heating and dynamic forces, but also avoids unpredictable dynamic behavior due to turbulence, as well as unknown and seasonally variable atmospheric composition and temperature which has led to dangerous, mission-critical events. For exploration class missions such as DRA 5.0 aerocapture and Thermal Protection Systems (TPS) are proposed for breaking at Mars for cargo missions. Traditional aerocapture is considered too risky for manned missions. Even with the enormous mass savings that aerocapture allows, it still requires 80 tons of aeroshell and significantly increased launch mass and propellant. As will be shown, by using Magnetoshells for aerobraking, the DRA 5.0 mission will save 224 metric tons (MT) and greater than $2 B in launch costs. Beyond the dramatic savings for existing mission architectures, a low-mass, risk-free aerocapture system would allow much more rapid missions to Mars and deep space orbiters by allowing direct, faster trajectories. As will be shown, the plasma Magnetoshell Aerobraking, Aerocapture, and Entry System (AAES) not only reduces mass and cost while enabling significant new mission architectures, but also significantly reduces radiation exposures by decreasing trip times.

Manned Missions↗

NASA NTRSDate not supplied

CERES FluxByCldTyp NB2BB Fluxes Improvement based on Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗

NASA NTRSDate not supplied

Flux Improvement based on Machine Learning for the CERES FluxByCldTyp Data Product

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗

NASA NTRSDate not supplied

Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere and surface flux data for climate studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models. The FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate their broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗

NASA NTRSDate not supplied

Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗

NASA NTRS2022

Revisiting the Ground Magnetic Field Perturbations Challenge: A Machine Learning Perspective

Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change (dB H /dt) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind.

Victor A. Pinto↗

DOE OSTI2024

Measurement of Nuclear Dependence in Inclusive Antineutrino Scattering

One of the fundamental questions in physics is the matter-antimatter asymmetry of the Universe, explored through charge-parity violation searches in neutrino oscillation experiments. Next-generation oscillation experiments like DUNE need to constrain the systematic uncertainties arising from our understanding of (anti)neutrino-nucleus scattering to within a few percent to achieve their physics goals. MINERvA, a dedicated (anti)neutrino-nucleus scattering experiment, plays a crucial role in constraining these uncertainties. This thesis presents MINERvA's first high-statistics direct measurement of nuclear dependence in charged-current inclusive antineutrino scattering on carbon, hydrocarbon, iron, and lead as a function of antimuon transverse momentum, $p_{\text{T}}$, and Bjorken~$x$. The analysis utilises interactions with a mean antineutrino energy of approximately~6~GeV, with reconstructed antimuons having a scattering angle of less than 17$^\circ$ relative to the antin eutrino beam and an antimuon energy of 2--20 GeV. The measured per-nucleon differential cross-sections are reported with a precision of 7--9\%, while the cross-section ratios of carbon, iron, and lead to hydrocarbon have uncertainties of 5\% or less. The cross-sections for iron and lead indicate strong suppression at low $p_{\text{T}}$ and Bjorken~$x$, and an enhancement at high $p_{\text{T}}$. These effects are observed to be more pronounced with the increasing size of the target nucleus and are not reproduced by the underlying simulation prediction. Comparisons to alternative models used in current (anti)neutrino interaction generators show some improvements in modelling over the base prediction model, yet they are still unable to fully reproduce the observed nuclear dependence in this analysis. Importantly, the analysis provides a direct test of nuclear effects in inclusive antineutrino scattering, with major contributions from resonant pion production, deep inelastic scattering, and the transition region between these channels, which will be significant in DUNE. This measurement also represents one of the largest antineutrino datasets in this energy regime analysed to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRSDate not supplied

Reflectivity Control Device (RCD) Roll Momentum Management for Solar Cruiser and Beyond

Reflectivity control devices (RCDs), thin film optical devices that are actuated (by application of a voltage across the device) to control the reflection of sunlight on their surfaces, can effectively enable roll momentum management (MM) of a solar sail operating in deep space, outside any magnetic field. This enables solar sails to maintain adequate attitude control in these challenging environments without sacrificing mission life by using expendables. They were planned and developed for use on Solar Cruiser, operating sunward of the Sun-Earth Lagrange Point 1 (sub-L1) in a propulsion-intensive artificial halo orbit. Located in pairs of panels near the distal end of each of four booms, each articulated at a slight angle out of the sail plane to produce a force tangent to the sail at a large moment arm, the RCDs provide roll torque capabilities by using simple switching logic to determine which panels should be activated (voltage applied) to produce torque in the desired direction. The performance capabilities and constraints of these devices across a range of operating conditions, namely sun-relative attitude, is well characterized, granting designers insight on how to properly size the system and optimize the system and component designs to maximize roll torque per unit area. With an informed design, RCDs can be very effective actuators for managing roll momentum with minimal detrimental system impacts (mass, power, complexity, etc.) on Solar Cruiser and other future deep space solar sail missions.

Andrew F Heaton↗

DOE OSTI2022

SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are available at https://github.com/Alexanders101/SPANet

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1974

The structure and circulation of the deep Venus atmosphere

A simple model for the structure of a nonrotating Hadley regime in an atmosphere with large thermal inertia is developed. The radiative fluxes are estimated by using a linearization about the radiative equilibrium state, and the dynamical fluxes are estimated by using scaling analysis. The requirement that differential heating by these fluxes be in balance in both the meridional and vertical directions leads to two equations for the mean static stability and meridional temperature contrast. The solution depends on two parameters: the strength of the radiative heating, as measured by the static stability of the radiative equilibrium state; and the ratio of the time it takes an external gravity wave to traverse the atmosphere to the time it would take the atmosphere to cool off radiatively. It is shown that it is not necessary to invoke convection to explain the approximate adiabatic lapse rate in the Venus atmosphere, but a greenhouse effect is necessary to explain the high surface temperatures.

Stone, P. H.↗