A PINN-driven game-theoretic framework in limited data photoacoustic tomography. [PDF]
Inverse ProblRoy S, Pal S.
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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data [PDF]
, 2020 Liu Yang +2 more
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Information-distilled physics informed deep learning for high order differential inverse problems with extreme discontinuities. [PDF]
Commun EngPeng M, Tang H.
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Inf-sup stable space-time Local Discontinuous Galerkin method for the heat equation. [PDF]
J Sci ComputGómez S, Perinati C, Stocker P.
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Adaptive regularization and discretization for nonlinear inverse problems with PDEs
, 2014 In this thesis, efficient methods for the solution of inverse problems, combining adaptive regularization and discretization are proposed. For the computation of a Tikhonov regularization parameter, we consider an inexact Newton method based on Morozov's discrepancy principle. In each step, a regularized problem is solved on a different discretization
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Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow. [PDF]
J Comput Nonlinear DynLu T +5 more
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Scientific Machine Learning for Elastic and Acoustic Wave Propagation: Neural Operator and Physics-Guided Neural Network. [PDF]
Sensors (Basel)Mehtaj N, Banerjee S.
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Assessment of Spatially-Varying Arterial Wall Stiffness and Pressure Using a Physics-Informed Neural Network and Pulse Wave Imaging: An in Silico and Experimental Phantom Study of Stenotic Vessels. [PDF]
Ultrasound Med BiolRoy T +4 more
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New modifications of ADM with applications in kind of PDEs. [PDF]
MethodsXNemah EM, Jaddoa AF, Ghazi FF.
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One-shot learning for solution operators of partial differential equations. [PDF]
Nat CommunJiao A, He H, Ranade R, Pathak J, Lu L.
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