Control of partial differential equations via physics-informed neural networks

HIGHLIGHTS

• who: Carlos J. Garcu00eda-Cervera from the Department of Mathematics, University of California, Santa Barbara, CA, USA have published the Article: Control of Partial Differential Equations via Physics-Informed Neural Networks, in the Journal: (JOURNAL)
• what: The main motivation for exploring the use of these new techniques in approximating PDEs is not to try to find methods that outperform classical methods (finite differences, finite elements, finite volumes, etc.) in low spatial dimensions (d=1, 2, 3), but to solve numerically high-dimensional PDEs (d > 3), where the above-mentioned classical methods get stuck by the . . .
  

   

  

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