Generalizing semi-supervised generative adversarial networks to regression using feature contrasting
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Olmschenk, Greg↗
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Results for “network generation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection
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Randhawa, Rizwan Hamid (ORCID:0000000315635239)↗
Two-dimensional total absorption spectroscopy with conditional generative adversarial networks
Not provided.
Instruments & Instrumentation↗
CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Co-Simulation Framework For Network Attack Generation and Monitoring
Not Available
24 POWER TRANSMISSION AND DISTRIBUTION↗
Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks
Not Available
Yuan, Jingyi↗
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties.
Abstract not provided.
Kadeethum, Teeratorn↗
Utilization of the critic subnetwork of a generative adversarial network as detector of morphological material change in image data.
Abstract not provided.
Beste, Ariana↗
Subsurface Characterization using Deep Generative Adversarial Networks with Ensemble-based Optimization.
Abstract not provided.
Bao, Jichao↗
Coupling Self Attention Generative Adversarial Network and Bayesian Inversion for Carbon Storage System
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Bao, Jichao↗
Modeling the deformation of complex additively manufactured polycrystalline alloys using Generative Adversarial Networks and Crystal Plasticity
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36 MATERIALS SCIENCE↗
Enhanced Geothermal Site Characterization using Generative Adversarial Network and Ensemble Method
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Bao, Jichao↗
Improving unfolding and systematic uncertainty estimation using generative diffusion networks (Final Technical Report)
This final technical report summarizes the key accomplishments on the unfolding using diffusion model project, a DOE award received by PI Pierre-Hugues Beauchemin at Tufts University. This project main goal was to investigate the potential of diffusion models for unfolding experimental High Energy Physics data from detector effects while controlling systematics uncertainties. The project accomplished its goals by completing the following objectives: 1) Performing an object-by-object, event-by-event unfolding of various kinematic distributions reconstructed from detector data in HEP in a way that keeps correlations between unfolded observables while demonstrating competitive performance compared to standard algorithms used in the field; 2) Address the generalization problem by developing an unfolding algorithm capable to correctly infer the underlying distributions of observables and processes never seen before, while controlling the dominant theoretical uncertainties affecting the process, therefore increasing the effectiveness, the precision, and the applicability of the developed algorithm; 3) Understand the theoretical foundations between the developed algorithm so to extend it to applications beyond experimental HEP, for broader benefits to the society. This report provides an overview of the accomplishments related to each of these key objectives.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Enhanced Geothermal Site Characterization using Generative Adversarial Network and Ensemble Method
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Bao, Jichao↗
Application of the CINGEN program a thermal network data generator
The application of the CINGEN computer program and two of its supporting programs for the evaluation of structural and thermal performance of physical systems was described. The CINGEN program was written and implemented to avoid the duplication effort of performing a finite element approach for structural analysis and a finite differencing technique for thermal analysis, as well as the desire for a geometrical representation of the thermal model to reduce modeling errors. The program simplifies the thermal modeling process by performing all of the capacitance and conductance calculations normally done by the analyst. Each solid element is divided into five tetrahedrons, allowing the total volume to be calculated precisely. A sample problem was illustrated.
Shultz, W. E.↗