PDE-LEARN is a PDE discovery algorithm that Robert Stephany and Christopher Earls developed at Cornell University. It can identify a wide variety of linear and non-linear Partial Differential Equations (PDEs) directly from noisy and limited data. This README explains how to use PDE-LEARN. Throughout this README, we assume the reader is familiar with the PDE-LEARN algorithm. For a complete description of PDE-LEARN, see the paper PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited Data. If you have questions about our approach and can not find the answer in this document or the paper, send your question to the corresponding author at rrs254@cornell.edu.

Library structure

We split the PDE-LEARN repository into several sub-directories and files. The most important of these are Settings.txt and Library.txt. These files control every variable in the PDE-LEARN algorithm. Both files are in PDE-LEARN's main directory. Settings.txt houses the settings that control how PDE-LEARN operates, while Library.txt specifies the left and right-hand side terms of the library of candidate terms for the hidden PDE. When PDE-LEARN starts, it parses the contents of Settings.txt and Library.txt and then identifies a hidden PDE using the parsed settings. Below, we illustrate how to set up these files.

In general, you should not modify the underlying source code. If you want to use PDE-LEARN in a way that does not appear possible using the settings in Setting.txt, you are welcome to modify the code as you feel fit. With that said, the authors will not be able to help you debug any changes you make. If you decide to modify the source code, we advise you to do so with caution.

Code: This directory houses PDE-LEARN's source code. All of our code contains detailed comments. If you want to understand how PDE-LEARN works "under the hood," we encourage you to look through our source code. If you find a bug (or anything else that seems unusual) in our code, please tell us about it by emailing the corresponding author at rrs254@cornell.edu.

We split PDE-LEARN's source code over several files. We describe each file below.

main.py is the file you should call when you want to run PDE-LEARN (see below). This file reads the settings and library, sets up the system response functions, initializes $\xi$, trains everything, and then saves the results. It drives the rest of the code.

Data.py loads a saved data set (in the .npz file format). See our description of the Data sub-directory below for more details.

Evaluated_Derivatives.py houses code that can compute $D_a U$ from $D_b U$ where $D_a$ and $D_b$ are partial derivative operators and $U$ is a neural network. This function assumes that $D_b$ is a "child derivative" of $D_a$, which essentially means that every partial derivative that appears in $D_b$ also appears in $D_a$ and has at least the same order. If $D_a$ is a "child derivative" of $D_a$, then it is possible to compute $D_a U$ from $D_b U$ by taking additional partial derivatives of $D_b U$. Our code exploits this fact to improve the algorithm's runtime and avoid calculating the same partial derivatives multiple times.

Loss.py houses code that computes the data, collocation, and $L^p$ loss functions. The collocation loss function computes the partial derivatives of the system response functions using the function Evaluate_Derivatives. To maximize efficiency, PDE-LEARN first computes low-order partial derivatives of the system response functions and then uses these to compute computing higher-order partial derivatives. This approach minimizes the number of computations required to evaluate the library terms.

Plot.py houses code to plot loss histories. Each time the user runs PDE-LEARN, it produces plots that depict the data, collocation, $L^2$, and total loss of each system response function, along with $L^p$ loss of $\xi$. The $L^2$ loss of a system response function is the square of the $L^2$ norm of that network's parameters. PDE-LEARN then saves these plots in the Figures directory.

Points.py houses a function that generates the random collocation points.

Test_Train.py houses two functions: Training and Testing. The Training function evaluates the system response functions on the testing data set and testing collocation points. Training then computes the loss functions using points and uses these values to update $\xi$ and each system response function. By contrast, the Testing function evaluates the network/library's performance on a data set but does not update the network's parameters or components of $\xi$. Crucially, Testing does NOT modify any network parameters; its purpose is to check if the system response functions are over-fitting.

The Classes sub-directory houses definitions of the various classes that PDE-LEARN uses.

Finally, the Readers sub-directory houses code that parses Settings.txt and Library.txt.

Data: PDE-LEARN trains each system response function to match a data set. The "DataSet Names" setting specifies the data sets that PDE-LEARN trains on. "DataSet Names" should be a list of strings specifying files in the Data/DataSets directory. A "DataSet" is a .npz file that contains a dictionary with six keys: "Training Inputs," "Training Targets," "Testing Inputs," "Testing Targets," "Bounds," and "Number of Dimensions." Each of these keys refers to a numpy.ndarray object (except "Number of Dimensions," which is an integer that specifies the number of spatial dimensions in the inputs within the data set).

In principle, you can split your dataset into a testing and training set. PDE-LEARN will update the system response functions using only the training set data. However, PDE-LEARN will report the loss on both the training and test sets. You can use this to determine if the solution networks are overfitting the training set. However, PDE-LEARN does not need a testing set to operate correctly. If you want to train the system response functions on all your data, set the testing set to some subset of your training set. Even in this case, PDE-LEARN will generate a separate set of collocation points during the testing step.

If you want to use PDE-LEARN on your data, you must write a program that calls the Create_Data_Set function (in Create_Data_Set.py) with the appropriate arguments. See that function's doc-string for details.

Alternatively, you can create a DataSet using one of our MATLAB data sets by running Python3 ./From_MATLAB.py when your current working directory is Data. The From_MATLAB file contains five settings: "Data_File_Name," "Num_Spatial_Dimensions," "Noise_Proportion," "Num_Train_Examples," and "Num_Test_Examples." "Data_File_Name" should refer to one of the .mat files in the MATLAB/Data directory. "Num_Spatial_Dimensions" specifies the number of spatial dimensions in the inputs stored in the .mat file. "Noise_Proportion," "Num_Train_Examples," and "Num_Test_Examples" control the level of noise in the data, the number of training data points, and the number of testing data points, respectively.

Plot: The Plot directory contains code for visualizing the networks that PDE-LEARN trains. In particular, it plots the network's predictions over the problem domain. You can use the file Plot/Settings.txt to set up these plots. The file has two settings: "Load File Name" and "Mat File Names." The former specifies the name of the save you want to visualize (this is the file that PDE-LEARN saves the system response functions to after training). The latter is a list of strings. The $i$th string should be the name of the .mat file that houses the noise-free data that made the noisy and limited data set you used to train the $i$th system response function. Critically, the "Load File Name" setting must refer to a file in Saves. To plot a saved system response function, set the appropriate settings in Plot/Settings.txt and then run Python3 ./Plot_Solution.py when your current working directory is Plot.

We need the entire noise-free dataset to evaluate the network's predictions. Therefore, PDE-LEARN currently only supports plotting for networks trained on a data set derived from one of the MATLAB files.

Figures: When PDE-LEARN makes a figure, it saves that figure to the Figures directory. Thus, the loss-history plots (that PDE-LEARN makes each time it runs) and the plots that Plot_Solution.py makes end up in this directory.

Saves: When PDE-LEARN serializes the network and library at the end of training, it saves the network's state, the library, $\xi$, and other relevant information to a file in the Saves directory. PDE-LEARN names the save by appending the network and optimizer type onto the end of the DataSet name(s). If you choose to load a file from save, the "Load File Name" setting in either Settings.txt or Plot/Settings.txt must refer to a file in the Saves directory.

Test: This directory contains the test code we used while developing PDE-LEARN.

MATLAB: This directory contains the MATLAB data sets (the .mat files in MATLAB/Data) and the scripts that create them (the .m files in MATLAB/Scripts).

Settings and the Library:

In general, to use PDE-LEARN you ONLY need to modify the contents of the files Settings.txt and Library.txt. In particular, you should not need to change any of PDE-LEARN's code; the settings and library files control everything. In this section, we discuss how to use both files. Roughly speaking, the Library.txt file defines the left and right-hand side terms ($f_0$ and $f_1, ..., f_K$, respectively; see the paper for more details), while Settings.txt controls everything else.

Settings.txt: First, let's discuss Settings.txt. We organized the settings into categories depending on what aspects of PDE-LEARN they control. Below is a discussion of each settings category.

Save, Load Settings: "Load U Network from Save," "Load Xi, Library from Save," and "Load Optimizer from Save" specify if you want to start training using a pre-saved system response function, library and $\xi$ vector, or optimizer state, respectively. If any of these settings is true, you must specify the "Load File Name" setting, whose should be a file in the Saves directory.

Note that if you plan to load the system response functions, $\xi$, or library from an existing state but want to train using a different optimizer, you CAN NOT load the optimizer state. In general, you can only load the optimizer state if the optimizer setting (see below) matches the optimizer you used to make the save.

Library Settings: This section contains just one setting: "Library File." Its value should be the name of the library file you want to use to build the library. Note that PDE-LEARN ignores this setting if "Load Xi, Library from Save" is set to true. Further note that the library file does NOT need to be called Library.txt. The library file can be any text file that adheres to the format of the Library.txt file included in this library.

Network Settings: These settings control the architecture of the system response function network(s), $U_1, ... , U_S$. Each network has the same architecture. You specify the width of each layer, as well as the activation function. Each $U_k$ then adopts this architecture. Note that PDE-LEARN ignores the architecture settings if the "Load U from Save" setting is true. The "Hidden Layer Widths" setting should be a list of integers: the $i$th entry of this list specifies the number of neurons in the $i$th hidden layer of each $U_i$. Likewise, the "Hidden Activation Function" function specifies the activation function we apply after each hidden layer. Currently, PDE-LEARN supports three activation function types: Rational (or Rat), Hyperbolic Tangent (or Tanh), and Sine. We recommend using Rational (as we used this activation function for every experiment in the paper).

Finally, "Train on CPU or GPU" specifies if training should happen on a CPU or GPU. You can only train on a GPU if PyTorch supports GPU training on your computer's graphics card. Check PyTorch's website for details.

Loss Settings: "p" specifies the hyperparameter p in the $L^p$ loss (see the methodology section of the paper). Likewise, "Weights" is a dictionary that must have four keys: "Data," "Coll," "Lp," and "L2". The first three specify $w_{Data}$, $w_{Coll}$, and $w_{L^p}$ (See the methodology section of the paper), respectively. Finally, if the value corresponding to "L2" is $c \neq 0$, we add $c$ times the square of the $L^2$ norm of each system response function's parameters to the loss function. The $L^2$ norm acts as a regularizer (it is generally called "weight decay" in the Machine Learning literature). In practice, using a small but non-zero value for the "L2" weight (on the order of $1e-5$) can slightly improve PDE-LEARN, though keeping this weight at $0$ generally works fine as well.

The "Number of Training Collocation Points" and "Number of Testing Collocation Points" settings control the number of RANDOM testing and training collocation points, respectively. Recall that PDE-LEARN uses two different kinds of collocation points: Random and targeted. PDE-LEARN re-selects the random collocation points at the start of each epoch and selects the targeted ones based on where the PDE residual is largest (see the methodology section of the paper).

Finally, if "Mask Small Xi Components" is true, PDE-LEARN will stop learning all components of $\xi$ whose initial magnitude is smaller than $0.0005$ (we discuss the reasoning behind this value in the paper). Note that PDE-LEARN ignores this setting unless you are loading $\xi$ from a save (if "Load Xi, Library from Save" is true).

Optimizer Settings: These settings control how PDE-LEARN trains $\xi$ and the system response function networks. The "Optimizer" setting specifies which optimizer to train the networks. PDE-LEARN supports two optimizers: Adam and LBFGS. Note that we used the Adam optimizer in all of our experiments in the paper. The "Number of Epochs" and "Learning Rate" settings specify the number of epochs and the optimizer learning rate, respectively.

Data settings: These settings specify where PDE-LEARN gets the data it uses to train the system response functions. The "DataSet Names" setting should be a comma-separated list of strings. The ith string should specify the name of a DataSet file. See the Data section above to understand how to create DataSet files. PDE-LEARN makes one system response function per entry in this list. Critically, PDE-LEARN saves the data set names when it saves the networks. Thus, if you load the system response function networks from a save, PDE-LEARN will ignore this setting.

Library.txt: Now that we know how to set up the Settings, let's discuss Library.txt. You must specify two settings in the Library file: The left-hand side term and the right-hand side term. To specify the right-hand side terms, place one term per line. The Library.txt file that comes with this repository includes details on how to format a particular library term. Please see that file and the enclosed instructions when setting up your library file. Finally, note that the library file does NOT need to be named Library.txt. It can be any text file that adheres to the format specified in the Library.txt file included in with repository.

Running the code:

Once you have selected the appropriate settings, you can run the code by entering the Code directory (cd ./Code) and running the main file (Python3 ./main.py).

Burn In: The first step is the burn-in step. For this step, set all of the "load" settings to false. Next, select your library and network architecture. For the loss settings, set the "Data" and "Coll" weights to $1.0$ and the "Lp" weight to $0.0$. Make sure that "Mask Small Xi Components" is true. Note that this setting will not do anything until the later stages. For the burn-in step, we recommend training for $1,000$ epochs using the Adam optimizer with a learning rate of $.001$. Select the data sets you want to train on and run the code. Make sure to watch the data loss during this stage. If the data loss appears to stop decreasing after a few hundred epochs, consider re-running this stage with fewer epochs. In general, letting PDE-LEARN train the system response functions after the data loss plateaus (stops decreasing) encourages over-fitting and can reduce the accuracy of the final identified PDE. In our experience, the data loss stops dropping after $\approx 600-800$ epochs, though it can take more or less depending on the data sets. If the data loss is decreasing reasonably quickly after $1,000$ epochs, you can continue training by loading from the save that PDE-LEARN made after the first $1,000$ epochs. We suggest training for a few hundred more epochs and then checking if the data loss is still decreasing. If so, continue training for more epochs, always loading from the most recent save. Once the data loss plateaus, you have finished the burn-in step.

Sparsification: The second step is the sparsification step. For this step, set all of the "load" settings to true. Set the "Load File Name" setting to the name of the save from the end of the burn-in step. Change the "Lp" weight to a small, positive value like $0.0002$. Otherwise, you should use the same settings that you used in the burn-in stage (note that PDE-LEARN will ignore any changes you make to the architecture and or data settings). We recommend training for another $1,000$ epochs using the Adam optimizer with a learning rate of $0.001$. Run the code and watch the Lp loss as it runs. If the Lp loss has not decreased significantly in $\approx 200$ epochs, you can probably stop training. Usually, this takes around $1,000$ epochs, though it sometimes takes more. After training, look at the "Lp loss history" plot. The plot should look like a staircase (with each step corresponding to one of the components of $\xi$ dropping to zero). If you think the Lp loss might drop down more "steps," you can run the sparsification step for additional epochs (loading from the save produced at the end of the first $1,000$ epochs of training). Once the Lp loss stabilizes, you have finished the sparsification step.

Fine-tuning: The fine-tuning step is the final stage. For this step, keep all of the "load" settings to true, but change the "Load File Name" setting to the name of the save from the end of the sparsification step. Change the "Lp" weight to $0.0$. Otherwise, we recommend using the same settings from the sparsification stage. We recommend training for $1,000$ epochs using the Adam optimizer using a learning rate of $.001$. However, the optimal number of epochs in this step depends significantly on the underlying data set. In some experiments, we used $50$ or fewer fine-tuning epochs. In other experiments, we used several thousand. When this step runs, closely watch the Lp loss. Almost always, a pattern emerges: the Lp loss increases for a while, plateaus, and then decreases. You want to stop training when the Lp loss plateaus. One way to do this is to train for a few thousand epochs. Once you see the Lp loss plateau, record the number of epochs and then kill PDE-LEARN (by pressing Ctrl + C). Set the number of epochs to the number you wrote down and re-run PDE-LEARN. This approach usually yields the most accurate constants. We want to emphasize, however, that the fine-tuning step does NOT change the FORM of the identified PDE, only the constants in it. Stopping training before or after the "plateau" in the Lp loss will not change the form of the identified PDE but decreases the accuracy of its constants.

What to do if you get nan: PDE-LEARN can use the LBFGS optimizer. Unfortunately, PyTorch's LBFGS optimizer is known to yield nan (see pytorch/pytorch#5953). Using the LBFGS optimizer occasionally causes PDE-LEARN to break down and start reporting nan. If this happens, you should kill PDE-LEARN (in the terminal window, press Ctrl + C) and then re-run PDE-LEARN. Since PDE-LEARN randomly samples the collocation points from the problem domain, no two runs of PDE-LEARN are identical. Thus, even if you keep the settings the same, re-running PDE-LEARN may avoid the nan issue. If you encounter nan on several successive runs of PDE-LEARN, reduce the learning rate by a factor of $10$ and try again. If all else fails, consider training using another optimizer.

Dependencies:

PDE-LEARN will not run unless you have installed the following:

• Python3
• numpy
• torch
• matplotlib
• pandas
• seaborn

Additionally, you'll need scipy if you want to use the From_MATLAB.py function in the Data directory.