FBPINNs
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benmoseley
Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)
Hello，I am trying to use your program to write a FBPINN that solves a 3D temperature field, because the problem is more complex, I use the soft-constrained loss function form...
 Thank you for the innovative contribution! I tried modifying the wave 3D problem to have the following boundary conditions: _u(x,y,0) = 0 u(0,0,t) = 2 sin (2 pi t)...
Hello, I am getting an error while running the file paper_main_1D.py. I am using Spyder IDE on Anaconda. "OSError: [WinError 1455] The paging file is too small for this operation...
Your code is awesome! 👍 And i have some difficulties in understanding your code. ``` # codes from main.full_model_FBPINN y = y * c.Y_N[1] + c.Y_N[0] ``` I think this...
Dear Ben, Thank you for this creative work! I saw in the code that you can use two types of c values; one is obtained by `_constant_c()` and the other...
I am a beginner and I would like to know if this method requires a dataset or data for training, which I didn't find in the case, thanks
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Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)