whinvik

28 ms·

1. 271.▲
   What is the Navier Stokes problem?(twitter.com)

   3 points by whinvik 6y ago|0 comments
2. 272.▲
   by whinvik 6y ago

   There are multiple reasons but they all can be application specific. First reason is that quadrilaterals can be mapped to a square and so you can use "Tensor product" elements which is just a fancy way of saying that you can uncou
3. 273.▲
   by whinvik 6y ago

   Yes, it can be difficult. I now think a lot before following someone so my feed is pretty politics free, so it is possible.
4. 274.▲
   Einstein's PhD Thesis(mobile.twitter.com)

   2 points by whinvik 6y ago|3 comments
5. 275.▲
   by whinvik 6y ago

   High order meshing is becoming increasing important in Computational Fluid Dynamics since high order numerics requires the boundaries to be more accurately represented. Would be interesting to know if this method could be extended to genera
6. 276.▲
   Short History of Fluid Mechanics(twitter.com)

   4 points by whinvik 6y ago|0 comments
7. 277.▲
   by whinvik 6y ago

   I think a lot of machine learning applied to computational science problems is basically function approximation. However a lot of computational science problems actually are => here's a good basis function to approximate functions,
8. 278.▲
   by whinvik 6y ago

   Well, it helps to see these connections. For example, realising that the lowest order DG method is finite volume lets one think about how to extend well studied finite volume properties to high order DG methods.
9. 279.▲
   by whinvik 6y ago

   Its usually only finite difference methods that are not variational. But finite difference is dominant in academia, not in industry. And that is changing as well with methods such as the discontinuous Galerkin method. The more popular finit
10. 280.▲
    by whinvik 6y ago

    That maybe true but what I was looking for is a more convincing way of showing that a neural network approximates a function better than other functions, such as say a b-spline. For example, you say that the neural network with many more pa
11. 281.▲
    by whinvik 6y ago

    Thanks, not familiar with QM at all, but it seems to me from glancing through one of the papers that the neural network is used to replace a popular way of representing the wave function which itself is an Ansatz. Not very convincing, but o
12. 282.▲
    by whinvik 6y ago

    Almost every time a PDE is solved on a computer, it is a variational problem. Maybe neural networks are indeed good at this but I haven't seen any literature that shows that it is provably better. A reference would be good, especially