Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations. [PDF]
Chen C, Yang Y, Xiang Y, Hao W.
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Persistent de Rham-Hodge Laplacians in Eulerian representation for manifold topological learning. [PDF]
Su Z, Tong Y, Wei GW.
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Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy. [PDF]
Kandel S +9 more
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Infinitely many solutions for a class of differential inclusions involving the p-biharmonic
The existence of inffinitely many solutions for diffierential inclusions depending on two positive parameters and involving the p- biharmonic operator is established via variational ...
Barletta, Giuseppina, Livrea, Roberto
core
Spatial-Temporal EEG Imaging for Dual-Loop Neuro-Adaptive Simulation: Cognitive-State Decoding and Communication Gating in Critical Human-Machine Teams. [PDF]
Juárez R +3 more
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Inverse backscattering problem for perturbations of biharmonic operator
We consider the inverse backscattering problem for a biharmonic operator with two lower order perturbations in two and three dimensions. The inverse Born approximation is used to recover jumps and singularities of an unknown combination of potentials ...
Tyni, T. (Teemu), Harju, M. (Markus)
core
Unique continuation theorems for biharmonic maps. [PDF]
Branding V, Oniciuc C.
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Electrographic flow mapping for atrial fibrillation: theoretical basis and preliminary observations. [PDF]
Haines DE +6 more
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A stabilized Chebyshev-Galerkin approach for the biharmonic operator
We propose an efficient implementation of the Chebyshev Galerkin spectral method for the biharmonic operator. This discretization leads to banded matrices which, compared with other methods of the same type, are also better conditioned. The efficiency
I. S. Pop, Pop, I.S.
core
Stability of the surface diffusion flow and volume-preserving mean curvature flow in the flat torus. [PDF]
De Gennaro D, Diana A, Kubin A, Kubin A.
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