Fast way to calculate Standing Wave modes in NumPy
A few things to consider:


Loops in python are very inefficient. To optimize performance, when
it is possible to vectorize code, you should do so. 
Avoid calculating the same thing multiple times. For example, the term $$ cos(\omega_{n,m}t + \phi_{n,m}) $$ does not depend on x and y, so there is no need to calculate it during every iteration of the loop.
The number of calculations you are performing is huge. Nx * Ny * Nt * n_max * m_max is roughly 191 billion iterations, and on each of those iterations, multiple calculations are performed. I am not sure about the reason for using so many points on your x,y,t grid, but for adequate visualization, much fewer are required. If the goal is numerical precision, I would recommend checking out SciPy's interp2D function. What you could do is calculate a smaller number of grid points, and if you need to sample a value that is not on the grid, you can use the 2d interpolation function.


I have altered your code to vectorize it as much as possible. I did not completely vectorize out the loops, because my laptop does not have RAM capacity in the triple digits of gigabytes.

Reducing the number of grid points in the X and Y directions results in a huge speedup, and the resulting plot is shown after the code.

import numpy as np

#Nx = 384
#Ny = 384
#Nt = 360

Nx = 38
Ny = 37
Nt = 360

#########################################################
# function to calculate standing wave (original code had error in amp line)
#########################################################
def s_wave(lx, ly, xx, yy, tt, pp, max_modes):
    tot_amp = 0    # Initialize total amplitude to 0
    Mm      = 1000 # a constant relevant to my problem
    p_idx   = 0    # The index for accessing the phase noise

    for m in range (0,max_modes):
        for n in range (0,max_modes):
            #############################
            # Calculating Omega
            #############################
            omega1 = n*np.pi/(lx*Mm)
            omega2 = m*np.pi/(ly*Mm)

            omega  = np.sqrt(omega1**2 + omega2**2)

            #############################
            # Calculating Amplitude
            #############################
            amp1   = np.sin(n*np.pi*xx/lx)
            amp2   = np.sin(m*np.pi*yy/ly)
            amp3   = np.cos(omega*tt - pp[p_idx])

            amp    = amp1 * amp2 * amp3

            #############################
            # Update total amplitude
            #############################
            tot_amp = tot_amp + amp
            p_idx   = p_idx + 1

    return tot_amp

def s_wave_opt(lx, ly, xx, yy, tt, pp, max_n, max_m):
    tot_amp = 0    # Initialize total amplitude to 0
    Mm      = 1000 # a constant relevant to my problem

    #############################
    # Initialize total amplitudes at all points to 0
    #############################
    A_total = np.zeros([len(xx), len(yy), len(tt)])

    #############################
    # Calculate omega
    #############################
    n,m     = np.mgrid[1:max_n+1:1, 1:max_m+1:1]
    omega_n = n * np.pi / ( lx * Mm )
    omega_m = m * np.pi / ( ly * Mm )
    omega   = np.sqrt(omega_n**2 + omega_m**2)

    #############################
    # Vactorize calculation of term3
    #############################
    term3 = np.multiply.outer(t,omega)
    term3 = np.add(pp,term3)
    term3 = np.cos(term3)

    #############################
    # Calculating A[x,y,:]
    #############################
    nr     = np.arange(1,max_n+1)
    term1  = np.multiply.outer(xx, nr*np.pi/(lx*Mm))
    term1  = np.sin(term1)

    mr     = np.arange(1,max_m+1)
    term2  = np.multiply.outer(yy, mr*np.pi/(ly*Mm))
    term2  = np.sin(term2)

    for y_idx in range(len(yy)):
        xy  = np.multiply.outer(term1, term2[y_idx])
        xyt = np.multiply(xy[:,None,:,:],term3[None,:,:,:])
        tot = np.sum(xyt, axis=(2,3))
        A_total[:,y_idx,:] = tot

    return A_total


# building grid points in x and y direction
Lx = 6.144 # Length of the Box along each axis
Ly = 6.144
T = 10 # delta_T 
x = np.linspace(-Lx/2, Lx/2, Nx)
y = np.linspace(-Ly/2, Ly/2, Ny)
t = np.linspace(0, (Nt-1)*T, Nt)

modes_n = 60
modes_m = 61
phase2  = np.random.uniform(0,2*np.pi, [modes_n,modes_m])
ustand  = s_wave_opt(Lx, Ly, x, y, t, phase2, modes_n, modes_m)


The resulting plot was too large to attach as a gif, but attached is a screenshot of one frame:


The code to produce the gif plot:

##################################################
# Plotting the results
##################################################
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt

import os
if not os.path.isdir("./images"):
    os.system("mkdir images")

for i in range(Nt):
    fig = plt.figure()
    ax  = plt.axes(projection='3d')
    X,Y = np.meshgrid(y,x)
    ax.plot_surface(X, Y, ustand[:,:,i])

    ax.set_xlim3d(x.min(), x.max())
    ax.set_ylim3d(y.min(), y.max())
    ax.set_zlim3d(ustand.min(), ustand.max())
    ax.set_title("Standing Wave at Time {}".format(t[i]))
    plt.savefig("./images/frame{}.png".format(i))
    plt.close()


os.system("ffmpeg -i ./images/frame%d.png -vf palettegen -y paletter.png &amp;&amp; ffmpeg -framerate 20 -loop 0 -i ./images/frame%d.png -i paletter.png -lavfi paletteuse -y plot.gif")


The code runs in about 4 minutes. Roughly 1 minute are the calculations, and the rest is time spent creating the graph.