Results 311 to 320 of about 4,008,449 (360)
Lipid Transporter ABCA1 in Diabetic Polyneuropathy: Potential Mediation by Circulating Metabolites. [PDF]
J Pain ResCao Y +8 more
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From static to dynamic: how object rotation influences grasp decisions in ambiguous settings. [PDF]
Exp Brain ResMaffitt NJ +4 more
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Stability of Inverse Problems for Steady Supersonic Flows Past Lipschitz Perturbed Cones. [PDF]
Arch Ration Mech AnalChen GG, Pu Y, Zhang Y.
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, 2023
This book discusses the engineering problem of determining surface heat flux and temperature history based on interior temperature measurements. It also provides the analytical techniques needed to arrive at otherwise difficult solutions, summarizing the
K. Woodbury +3 more
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This book discusses the engineering problem of determining surface heat flux and temperature history based on interior temperature measurements. It also provides the analytical techniques needed to arrive at otherwise difficult solutions, summarizing the
K. Woodbury +3 more
semanticscholar +1 more source
Inverse design in nanophotonics
Nature Photonics, 2018Recent advancements in computational inverse-design approaches — algorithmic techniques for discovering optical structures based on desired functional characteristics — have begun to reshape the landscape of structures available to nanophotonics.
S. Molesky +5 more
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Obstetric Anesthesia Digest
(BJA Educ. 2024;24(4):109e112. doi:10.1016/j.bjae.2024.01.004. Epub February 2024.) Uterine inversion (UI) is a serious condition where the uterus protrudes into the endometrial cavity and in more extreme cases, through the vaginal orifice.
S S, Pararajasingam +2 more
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(BJA Educ. 2024;24(4):109e112. doi:10.1016/j.bjae.2024.01.004. Epub February 2024.) Uterine inversion (UI) is a serious condition where the uterus protrudes into the endometrial cavity and in more extreme cases, through the vaginal orifice.
S S, Pararajasingam +2 more
openaire +2 more sources
IMA Journal of Numerical Analysis, 2020
Physics-informed neural networks (PINNs) have recently been very successfully applied for efficiently approximating inverse problems for partial differential equations (PDEs).
Siddhartha Mishra, R. Molinaro
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Physics-informed neural networks (PINNs) have recently been very successfully applied for efficiently approximating inverse problems for partial differential equations (PDEs).
Siddhartha Mishra, R. Molinaro
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SIAM Journal on Mathematical Analysis, 1982
This paper develops a formula of inversion for an integral transform of a type similar to that associated with the names of Kontorovich and Lebedev except that the kernel involves the Neumann function $Y_u (kr)$ and the variable r varies over the truncated infinite interval $a \leq r 0$.
Naylor, D., Chang, P. H.
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This paper develops a formula of inversion for an integral transform of a type similar to that associated with the names of Kontorovich and Lebedev except that the kernel involves the Neumann function $Y_u (kr)$ and the variable r varies over the truncated infinite interval $a \leq r 0$.
Naylor, D., Chang, P. H.
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Inverse kinematic problem: Solutions by pseudoinversion, inversion and no-inversion
Behavioral and Brain Sciences, 1995AbstractKinematic properties of reaching movements reflect constraints imposed on the joint angles. Contemporary models present solutions to the redundancy problem by a pseudoinverse procedure (Whitney 1969) or without any inversion (Berkenblit et al. 1986). Feldman & Levin suggest a procedure based on a regular inversion.
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Astrophysics and Space Science, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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