FAQ
If you have any questions about DeepXDE, first read the papers/slides and watch the video at the DeepXDE homepage and also check the following list of frequently asked DeepXDE questions. To get further help, you can open a discussion in the GitHub Discussions.
General usage
Q: DeepXDE failed to run.
Q: By default, DeepXDE uses float32
. How can I use float64
?
Q: I want to set the global random seeds.
-
Q: How can I use a trained model for new predictions?
Q: How can I save a trained model and then load the model later?
Q: More details about DeepXDE source code, and want to modify DeepXDE.
PINN
Q: What is the output of DeepXDE? How can I visualize the results?
A:
#668,
#9,
#675,
#48,
#53,
#73,
#77,
#171,
#217,
#218,
#223,
#274,
#276
Q: More details and examples about geometry.
Q: How can I implement new ODEs/PDEs, e.g., compute derivatives, complicated PDEs, complicated coefficients?
A:
#670,
#21,
#22,
#74,
#78,
#79,
#124,
#172,
#185,
#193,
#194,
#246,
#302,
#377,
#421,
#451,
#465,
#478,
#480
Q: More details and examples about initial conditions.
Q: More details and examples about boundary conditions.
A:
#6,
#669,
#673,
#674,
#22,
#26,
#33,
#38,
#40,
#44,
#49,
#115,
#140,
#156,
#352,
#365,
#457,
#475
Q: By default, initial/boundary conditions are enforced in DeepXDE as soft constraints. How can I enforce them as hard constraints?
Q: How can I use a dataset of the solution?
Q: I failed to train the network or get the right solution, e.g., large training loss, unbalanced losses.
A:
#673,
#22,
#33,
#41,
#61,
#62,
#80,
#84,
#85,
#108,
#126,
#141,
#188,
#247,
#305,
#321
-
Q: Implement certain features for the input, such as Fourier features.
Q: A standard network doesn’t work for me, and I want to implement a network with a special structure/property.
Q: Implement new losses or constraints.
Q: How can I implement new IDEs?
Q: Solve PDEs with complex numbers.
Q: Solve inverse problems with unknown parameters/fields in the PDEs or initial/boundary conditions.
Q: Solve parametric PDEs.
Q: How does DeepXDE choose the training points? How can I use some specific training points?
Q: How can I give different weights to different residual points?
Q: Residual-based adaptive refinement (RAR).
Q: I want to customize network training/optimization, e.g., mini-batch.
Q: I want to get more information about the network, such as the values and gradients of hidden layers.
Q: More details about DeepXDE source code, or I want to modify DeepXDE.
A:
#35,
#39,
#66,
#69,
#91,
#99,
#131,
#163,
#175,
#202,
#357
Q: Examples collected from users.
Multi-fidelity NN
-
Q: Transfer learning of MFNN.
Q: How can I use a trained model for new predictions?