Artificial Neural Networks
Artificial neural networks works pretty well for some equation solving.
Universal Approximators
Mawell Stinchcombe and Halber White proved that no theoretical constraints for the feedforward networks to approximate any measureable function. In principle one can use feedforward networks to approximate measurable functions to any accuracy.
However the convergence slows done if we have a lot of hidden units. There is a balance between accuracy and convergence rate. More hidden units means slow convergence but more accuracy.
Here is a quick review of the history of this topic.
Kolmogorov’s Theorem
Kolmogorov’s theorem shows that one can use finite number of carefully chosen continuous functions to exactly mix up by sums and multiplication with weights to a continuous multivariable fnction on a copact set.

Cybenko 1989
Cybenko proved that
is a good approximation of continuous functions because it is dense in continous function space. In this result, $\sigma$ is a continuous sigmoidal function and the parameters are real.

Hornik 1989
“Single hidden layer feedforward networks can approximate any measurable functions arbitrarily well regardless of the activation function, the dimension of the input and the input space environment.”
Reference: http://deeplearning.cs.cmu.edu/notes/Sonia_Hornik.pdf
Dense
Set A is dense in set X means that we can use A to arbitarily approximate X. Mathematically for any given element in X, the neighbour of x always has nonzero intersection.
Measurable Function
Basically it means continuous.
Activation Functions

UniPolar Sigmoid Function

Bipolar Sigmoid Function

Hyperbolic Tangent

Radial Basis Function

Conic Section Function
Solving Differential Equations
The problem here to solve is
with initial condition $y(0)=1$.
To construct a single layered neural network, the function is decomposed using
where $y(t_0)$ is the initial condition and $k$ is summed over.
Articifial Neural Network
Presumably this should be the gate controlling trigering of the neuron or not. Therefore the following expit function serves this purpose well,
One important reason for chosing this is that a lot of expressions can be calculated analytically and easily.
FermiDirac Distribution
Aha, the FermiDirac distribution.
With the form of the function to be solved, we can define a cost
which should be minimized to 0 if our struture of networks is optimized for this problem.
Now the task becomes clear:
 Write down the cost analytically;
 Minimized cost to find structure;
 Substitute back to the function and we are done.
Overfitting
It is possible that we could over fit a network so that it works only for the training data. To avoid that, people use several strategies.
 Split data into two parts, one for training and one for testing. A youtube video
 Throw more data in. At least 10 times as many as examples as the DoFs of the model. A youtube video
 Regularization by plugin a artifical term to the cost function, as an example we could add the . A youtube video
Neural Network and Finite Element Method
We consider the solution to a differential equation
Neural network is quite similar to finite element method. In terms of finite element method, we can write down a neural network structured form of a function ^{1}
where $\mathcal N$ is the neural network structure. Specifically,
The function is parameterized using the network. Such parameterization is similar to collocation method in finite element method, where multiple basis is used for each location.
One of the choice of the function $F$ is a linear combination,
and $A(x_i)$ should take care of the boundary condition.
Relation to finite element method
This function is similar to the finite element function basis approximation. The goal in finite element method is to find the coefficients of each basis functions to achieve a good approximation. In ANN method, each sigmoid is the analogy to the basis functions, where we are looking for both the coefficients of sigmoids and the parameters of them. These sigmoid functions are some kind of adaptive basis functions.
With such parameterization, the differential equation itself is parameterized such that
such that the minimization should be
at each point.
References and Notes
 Tensorflow and deep learning  without a PhD by Martin Görner.
 Kolmogorov, A. N. (1957). “On the Representation of Continuous Functions of Several Variables by Superposition of Continuous Functions of one Variable and Addition,” Doklady Akademii. Nauk USSR, 114, 679681.
 Maxwell Stinchcombe, Halbert White (1989). “Multilayer feedforward networks are universal approximators”. Neural Networks, Vol 2, 5, 359366.
 Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks

Freitag, K. J. (2007). Neural networks and differential equations. ↩