Results 251 to 260 of about 1,786,533 (325)
Automatic network structure discovery of physics informed neural networks via knowledge distillation. [PDF]
Nat CommunLiu Z +6 more
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Ionization potential depression model with the influence of neighboring ions in warm and dense plasmas. [PDF]
Sci RepWu C +5 more
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Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning methodology. [PDF]
Sci RepKim G, Kim J.
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Survey of normalized CTDI<sub>vol</sub> values across four major computed tomography vendors for use in the MIRDct software. [PDF]
J Appl Clin Med PhysDinwiddie LE +13 more
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ThoR: A Motion-Dependent Physics-Informed Deep Learning Framework with Constraint-Centric Theory of Functional Connections for Rainfall Nowcasting. [PDF]
Sci RepGia KT, Van HT, Thanh AP, Minh NP.
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Resource Letter CP-3: Computational physics
American Journal of Physics, 2023This Resource Letter provides information and guidance for those looking to incorporate computation into their courses or to refine their own computational practice. We begin with general resources, including policy documents and supportive organizations.
T. Atherton
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IEEE Signal Processing Magazine, 2022
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits.
K. Hammernik +6 more
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Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits.
K. Hammernik +6 more
semanticscholar +1 more source