forxltk

Seems that there are no sample points in (0, 0) or (1, 1) and the loss will be nan. You can sample those points manually as anchors. For example: ```...

You should rescale the problem and set the loss weights. See FAQ.

- Yes. - See [https://arxiv.org/pdf/2007.14527.pdf](url) for `NTK`.

The following code should help you ``` xx = x[:, 0:1] yy = x[:, 1:2] x_left = tf.greater(xx, 0.1) x_right = tf.less(xx, 0.3) y_left = tf.greater(yy, 0.2) y_right = tf.less(yy,...

> Dear @forxltk Tnx so much. yy = x[: , 0:2] or yy= x[: , 1:2] ? x_left_condition & ... or x_left & ... ? should I put this in...

``` layer_size = [3] + [15] + [1] activation = "tanh" initializer = "Glorot uniform" net = dde.maps.FNN(layer_size, activation, initializer) ``` There are only `x` and `y`, so it should...

> @forxltk You are right. I corrected this but again got the same error. Seems that `tf.where` work differently in tf1 and tf2. - tf1: K = tf.where(condition, 10.0\*tf.ones_like(xx), 1.0\*tf.ones_like(xx))...

You should learn something from the above code. BTW, you can also use `tf.logical_or` `tf.logical_and`, then everything should be easy.

> @forxltk Would you please let me know whether the below is correct: > > xx = x[:, 0:1] yy = x[:, 1:2] > > x_left = tf.greater(xx, 0.1) x_right...

@happyzhouch I think you can't use hard bc here. Otherwise, what is `y` in `(0, 0)` or other corners? For conflicting adjacent bc, see #579.