Results 31 to 40 of about 85,369,012 (181)
International Journal of Mathematics and Mathematical Sciences, 2014 Singular nonlinear initial-value problems (IVPs) in first-order and second-order partial differential equations (PDEs) arising in fluid mechanics are semianalytically solved. To achieve this, the modified decomposition method (MDM) is used in conjunction
Nemat Dalir
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Mathematics, 2023 Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and thus ensure that their predictions are consistent and stable with the physical laws.
Zhixiang Liu +4 more
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Inverse problems for elliptic equations with fractional power type nonlinearities
, 2022 We study inverse problems for semilinear elliptic equations with fractional power type nonlinearities. Our arguments are based on the higher order linearization method, which helps us to solve inverse problems for certain nonlinear equations in cases ...
Liimatainen, Tony +7 more
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Computational Inverse Problems for Partial Differential Equations (hybrid meeting) [PDF]
, 2020 Inverse problems in partial differential equations (PDEs) consist in reconstructing some part of a PDE such as a coefficient, a boundary condition, an initial condition, the shape of a domain, or a singularity from partial knowledge of solutions to the
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Entropy, 2023 Physics-informed neural networks (PINNs) are effective for solving partial differential equations (PDEs). This method of embedding partial differential equations and their initial boundary conditions into the loss functions of neural networks has ...
Kuo Sun, Xinlong Feng
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Partial data inverse problems for Maxwell equations via Carleman estimates [PDF]
, 2018 In this article we consider an inverse boundary value problem for the time-harmonic Maxwell equations. We show that the electromagnetic material parameters are determined by boundary measurements where part of the boundary data is measured on a possibly ...
Ola, Petri +7 more
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Neural Inverse Operators for Solving PDE Inverse Problems [PDF]
, 2023 A large class of inverse problems for PDEs are only well-defined as mappings from operators to functions. Existing operator learning frameworks map functions to functions and need to be modified to learn inverse maps from data.
Mishra, Siddhartha +3 more
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Generalized modes in Bayesian inverse problems [PDF]
, 2019 Uncertainty quantification requires efficient summarization of high- or even infinite-dimensional (i.e., non-parametric) distributions based on, e.g., suitable point estimates (modes) for posterior distributions arising from model-specific prior ...
Piiroinen, Petteri +3 more
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International Transactions on Electrical Energy Systems, 2023 In this paper, the development and application of the radial basis function-finite difference (RBF-FD) method and the RBF-finite difference time domain (RBF-FDTD) method for solving electrical transient problems in power systems that are defined by the ...
Duc-Quang Vu +2 more
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Solving Inverse Source Problems for linear PDEs using Sparse Sensor Measurements [PDF]
, 2016 Many physical phenomena across several applications can be described by partial differential equations (PDEs). In these applications, sensors collect sparse samples of the resulting phenomena with the aim of detecting its cause/source, using some ...
Pier Luigi Dragotti +3 more
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