  In this work, we address a foundational question in the theoretical analysis
of the Deep Ritz Method (DRM) under the over-parameteriztion regime: Given a
target precision level, how can one determine the appropriate number of
training samples, the key architectural parameters of the neural networks, the
step size for the projected gradient descent optimization procedure, and the
requisite number of iterations, such that the output of the gradient descent
process closely approximates the true solution of the underlying partial
differential equation to the specified precision?
