Automatic network structure discovery of physics informed neural networks via knowledge distillation. [PDF]
Liu Z +6 more
europepmc +1 more source
Path-OED for infinite-dimensional Bayesian linear inverse problems governed by PDEs
J. Nicholas Neuberger +3 more
openalex +1 more source
WF-PINNs: solving forward and inverse problems of burgers equation with steep gradients using weak-form physics-informed neural networks. [PDF]
Wang X, Yi S, Gu H, Xu J, Xu W.
europepmc +1 more source
A primer on variational inference for physics-informed deep generative modelling. [PDF]
Glyn-Davies A +4 more
europepmc +1 more source
Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series. [PDF]
Mortezanejad SAF +2 more
europepmc +1 more source
A PINN-driven game-theoretic framework in limited data photoacoustic tomography. [PDF]
Roy S, Pal S.
europepmc +1 more source
Information-distilled physics informed deep learning for high order differential inverse problems with extreme discontinuities. [PDF]
Peng M, Tang H.
europepmc +1 more source
Adaptive regularization and discretization for nonlinear inverse problems with PDEs
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
openaire +1 more source
Inf-sup stable space-time Local Discontinuous Galerkin method for the heat equation. [PDF]
Gómez S, Perinati C, Stocker P.
europepmc +1 more source

