PHYSICS INFORMED OPTIMIZATION OF PULSED POWER INERTIAL CONFINEMENT FUSION
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Porwitzky, Andrew James↗
SEARCH · Engineering Papers
Results for “Physics Informed”
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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Physics-informed Machine Learning Application for Geologic Carbon Storage
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Yoon, Hongkyu↗
Physics-Informed Intrusion Detection Using Deep-Packet Inspection of Wind Turbine Control Networks
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Berg, Jonathan Charles↗
Physics-informed machine learning applications for forward and inverse modeling
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Yoon, Hongkyu↗
Incorporating full-field diagnostic data into constitutive model discovery via physics informed neural networks
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Hamel, Craig↗
Calibrating Constitutive Models with Full-Field Data via Physics Informed Neural Networks
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Hamel, Craig↗
Application of Strain Functionals for Physics Informed Machine Learning
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Material Science↗
Compensating for Sintering Distortion in Additively Manufactured Copper using Physics-Informed Gaussian Process Regression
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Moran, Samuel↗
Schwarz-based Domain Decomposition Solutions for Data-Driven and Physics-Informed Models
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Wentland, Christopher Robert↗
Spiking Physics Informed Neural Networks on Loihi-2
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Theilman, Bradley Harold↗
Acoustic Scattering Simulations Via Physics-Informed Neural Network
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Nair, Siddharth↗
Physics-informed neural operator network: Acoustic simulations of arbitrary-shape scatterers
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Nair, Siddharth↗
Calibrating Constitutive Models with Full-Field Data via Physics Informed Neural Networks
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Hamel, Craig↗
Unsupervised physics-informed disentanglement of multimodal data
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Walker, Elise↗
Viscoelastic Free Surface Flows: From Computational Models to Experiments and Physics-informed Neural Networks
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Rao, Rekha Ranjana↗
Data-Driven and Physics informed AI approaches for manipulation
Workshop presentation on the future of work in the age of robotics & AI
Slightam, Jonathon↗
Spiking Physics Informed Neural Networks on Loihi-2
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Theilman, Bradley Harold↗
Physics-informed Deep Generative Models to Quantify Uncertainties in Geophysical Full-waveform Inversion
SSA oral presentation
Elmeliegy, Abdelrahman M↗