Physics-informed neural networks for fluid mechanics

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  • Опубликовано: 23 дек 2024

Комментарии • 31

  • @darkside3ng
    @darkside3ng Год назад +8

    Amazing explanation. Finally I have a clear image of PINNs :)
    Thank you.

    • @rvinuesa
      @rvinuesa  Год назад +2

      I am happy the video helps! 🙂

  • @sahand944
    @sahand944 Год назад +3

    Very helpful. Thanks for your time and effort.

  • @Kai-r6n7z
    @Kai-r6n7z 10 месяцев назад +2

    Interesting work and nice explanation!
    What I don't understand is why you don't need to include a model for the Reynolds stresses. Since the RANS equations are underdetermined, it doesn't seem obvious to me that the model converges to a physically meaningful solution. So, does the model not converge to a different solution each time depending on the initial weights, thus not guaranteeing the physical meaningfulness of the solution?

    • @rvinuesa
      @rvinuesa  10 месяцев назад +2

      The boundary conditions ensure that one converges to the right solution. Note that these include boundary conditions for the Reynolds stresses!

    • @Kai-r6n7z
      @Kai-r6n7z 10 месяцев назад +1

      @@rvinuesa
      Thank you very much for your response! Initially, I didn't realize that you included boundary conditions for the Reynolds stresses as well. However, it's still not clear to me that this boundary value problem should have a unique solution inside the domain, considering there are 6 variables but only 3 field equations.

  • @taimoorzain7585
    @taimoorzain7585 Год назад +4

    thank Ricardo Vinuesa really really thanks can you also for LES for turbulence

    • @VinuesaLab
      @VinuesaLab Год назад +3

      This could be an application too, although there are some challenges with PINNs in unsteady problems

  • @MrHaggyy
    @MrHaggyy Год назад +3

    Great video. Would be interesting to know more about what properties in model and data will drive computational complexety in PINN's and traditionell simulation. From my understanding PINN's are great for cheap iterative measurements to optimize the setup of an experiment. Once you found points of interesst you can set up expensive traditional experiments.

  • @manojkumar-cm2ym
    @manojkumar-cm2ym Год назад +2

    Great explanation, could you let me know, is there chance to estimate the value of drag/ coefficient in the the E- E model with this approach

    • @rvinuesa
      @rvinuesa  Год назад +1

      Absolutely! You can calculate it based on the computed pressure and wall-shear stress

  • @huseynyusifov2033
    @huseynyusifov2033 Год назад +3

    Hi professor, I am also working on PINNs to solve the radial flow diffusivity equation. But it keeps failing. Using the same PINNs I solved Burger's and Schrodinger which gave magnificent results. Do you have some recommendations?

    • @rvinuesa
      @rvinuesa  Год назад +2

      Maybe you can email me the details?

  • @sudharsung2429
    @sudharsung2429 Год назад +3

    Will this method work if we use libraries like DeepXDE or Neurodiffeq? Is there a difference in using this library and the traditional method used

    • @VinuesaLab
      @VinuesaLab Год назад +3

      This relies on the very basic automatic differentiation, which is needed in the back-propagation algorithm to train any neural network

  • @elganaelmehdi1697
    @elganaelmehdi1697 Год назад +3

    This is a great video! Thank you very much. I have a question about the input data. I noticed that you used (X, Y) as input, which I think represents the coordinates of the problem. and threfore you made predictions for U, V, and P. However, one thing I noticed is that we didn't provide the model the initial conditions as input for the problem. Could you please explain why we don't need initial conditions?

    • @elganaelmehdi1697
      @elganaelmehdi1697 Год назад +2

      @@rvinuesa thanks 👍👍👍

    • @VinuesaLab
      @VinuesaLab Год назад

      Happy to help if you have more questions :) @@elganaelmehdi1697

  • @abhishekjoshi1677
    @abhishekjoshi1677 Год назад +3

    Excellent video, Prof. I have been also working in developing PINNs for turbulence modelling. What would you say are the current challenges for PINNs for fluid dynamics. I believe it is difficult for NN to learn the small scales turbulence. Is it true?

  • @shahidali-dl7wj
    @shahidali-dl7wj Год назад +4

    Geart job sir...
    Very productive

  • @angtrinh6495
    @angtrinh6495 Год назад +3

    Great work, Prof.! Can you please explain more about the boundary condition? As I have viewed your code, all test cases use bc loss as MSE between the prediction of bc points (x,y) and bc velocities (u,v) which are observations/measurements/simulated data. Therefore, all boundaries here use the Dirichlet boundary condition? And the data is from either the analytical solution or numerical solver, isn't it?

    • @VinuesaLab
      @VinuesaLab Год назад +1

      That's correct, the boundary conditions are Dirichlet, and they are taken from the statistics of the DNS

    • @angtrinh6495
      @angtrinh6495 Год назад +1

      @@VinuesaLab Thank you for the explaination!

  • @MaryamSoltani-d3m
    @MaryamSoltani-d3m Год назад +3

    Thankyou! it was great and really useful. I want to ask a question about 2 terms in loss function which you used. One of them (supervised loss) you used only data in boundaries. While working with experimental data is not better that we expand supervised loss over all points which we have measured? I mean if we have measured data in any points it can help us or not?

    • @MaryamSoltani-d3m
      @MaryamSoltani-d3m Год назад +3

      @Ricardo Vinuesa Thanks for your kind response.

    • @VinuesaLab
      @VinuesaLab Год назад

      Happy to answer any other questions you may have!@@MaryamSoltani-d3m

  • @manfredbogner9799
    @manfredbogner9799 9 месяцев назад +2

    Sehr gut

  • @elsadignaser8987
    @elsadignaser8987 Год назад +3

    Thanks very much for such valuable animation
    Would you please share your email
    Thanks indeed