# PINN Collocation Solver
Once you've defined your problem, it must be compiled such that TensorDiffEq can build the loss function described
by the boundary conditions, initial conditions, and physics defined in the previous sections.
### Layer Sizes
Here is where we will define the neural network size and depth. Currently, most PINN approaches use dense fully connected
neural networks for function approximation. Fully-connected Neural Networks have some level of theoretical backing
that they will converge to a solution of the underlying function {cite}`pinkus1999approximation,chen1993approximations`, and this
theoretical backing has extended into the PINN framework {cite}`shin2020convergence`. With that being said, currently the only type of network supported in
TensorDiffEq is the fully-connected MLP network.
TensorDiffEq uses the [Keras API](https://keras.io/) for neural network construction. All you need to do is define a list of layer
sizes for your neural network. So, for a network with an `[x,t]` input, 4 layers deep, with 128 nodes, one would define
a layer size list of `[2,128,128,1]`.
For our problem we have been building in the previous sections, we can define layer sizes as such:
```{code} python
layer_sizes = [2, 128, 128, 128, 128, 1]
```
Or, if your problem is a function of `[x,y,t]`, then you could define the exact same network with an input layer with 3 nodes, i.e.
```{code} python
layer_sizes = [3, 128, 128, 128, 128, 1]
```
### Build and Train the Model
In order to compile the model, we first initialize the model we are interested in. Currently, forward solutions of PINNs are performed by
the `CollocationSolverND()` method.
#### Collocation Solver
The primary method of solving forward problems in TensorDiffEq is the collocation solver. This methodology identifies points
in the domain of the problem and collocates them to the solution via a loss function. Therefore, this is a natural application
for a neural network function approximation.
##### Instantiate the Model
The `CollocationSolverND()` solver can be initialized in the following way:
```{code} python
CollocationSolverND(assimilate=False)
```
Args:
- `assimilate` - a `bool` that describes whether the `CollocationSolverND` will be used for data assimilation
Note that very little in the solver is truly initialized when creating the `CollocationSolverND` instance, most comes later in the `compile` call.
##### Methods
```{code} python
compile(layer_sizes, f_model, domain, bcs,
isAdaptive=False,
col_weights=None,
u_weights=None,
g=None,
dist=False)
```
Args:
- `layer_sizes` - a `list` of `ints` describing the size of the input, hidden, and output layers of the FC MLP network
- `f_model` - a `func` describing the physics of the problem. More info is provided in [this section](../../physics/index.ipynb)
- `domain` - a `domain` object containing the collocation points, defined further [here](../../domain/index.ipynb)
- `bcs` - a `list` of BCs describing the problem
- `isAdaptive` - a `bool` describing whether the problem is solved adaptively using the [SA-PINN](https://arxiv.org/pdf/2009.04544.pdf)
- `col_weights` - a `tf.Variable` object containing the vector of collocation weights used in self-adaptive training, if enabled via `isAdaptive`
- `u_weights` - a `tf.Variable` object containing the vector of initial boundary weights used in self-adaptive training, if enabled via `isAdaptive`
- `g` - a `func` describing the lambda function described in the [SA-PINN framework](https://arxiv.org/pdf/2009.04544.pdf). This defaults to squaring the collocation weights if not explicitly defined.
Only applicable if `isAdaptive` is enabled.
- `dist` - a `bool` enabling distributed training across multiple GPUs
Model compilation is truly where the rubber meets the road in defining an inference model in TensorDiffEq. We compile the model using the `compile` method on the
`CollocationSolverND` method. This will build out the loss function in the solver by iterating through the [BCs](../../ic-bc/bc/index.ipynb) and the [IC](../../ic-bc/ic/index.ipynb)
that define your problem. The compile function will also pull in the collocation points and optimize your `f_model` function for running in graph-mode in Tensorflow.
```{code} python
fit(tf_iter, newton_iter,
batch_sz = None
newton_eager = True)
```
Args:
- `tf_iter` - an `int` dictating the number of iterated for the selected tensorflow optimizer
- `newton_iter` - and `int` dictating the number of L-BFGS iterations to be completed following the `tf_iter` iterations
- `batch_sz` - an `int` indicating the size of batches of collocation points fed into the solver
- `newton_eager` - a `bool` indicating whether the L-BFGS iterations will be executed eagerly
```{note}
Currently, `newton_eager` is demonstrating some stability issues as of release v0.1.1. Neither form of newton optimization (eager or graph)
is currently supported in a distributed environment.
Additionally, `batch_sz` is not a tunable parameter in a distributed environment when using the SA-PINN methodology.
Specific notes on optimizing for multi-GPU environments will be addressed later in this document.
```
#### References
```{bibliography} ../../references.bib
:style: unsrt
```