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💡 Pro tip: This is one of those techniques that will make you look like a data science wizard! Introduction to Differential Geometry - Made Simple!

Differential geometry is a mathematical discipline that uses techniques of differential calculus, integral calculus, linear algebra, and multilinear algebra to study problems in geometry. It deals with smooth shapes and spaces, known as manifolds.

This next part is really neat! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def sphere(u, v):
    r = 1
    x = r * np.cos(u) * np.sin(v)
    y = r * np.sin(u) * np.sin(v)
    z = r * np.cos(v)
    return x, y, z

u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
u, v = np.meshgrid(u, v)
x, y, z = sphere(u, v)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x, y, z, color='b', alpha=0.3)
plt.show()

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🎉 You’re doing great! This concept might seem tricky at first, but you’ve got this! Curves in Space - Made Simple!

A curve in space is a continuous function from an interval of real numbers to a space. In differential geometry, we study smooth curves, which have continuous derivatives up to some desired order.

Let’s make this super clear! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def helix(t):
    x = np.cos(t)
    y = np.sin(t)
    z = t
    return x, y, z

t = np.linspace(0, 10 * np.pi, 1000)
x, y, z = helix(t)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot(x, y, z)
plt.show()

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✨ Cool fact: Many professional data scientists use this exact approach in their daily work! Tangent Vectors and the Tangent Space - Made Simple!

The tangent space at a point on a manifold is the vector space containing all possible directions in which one can tangentially pass through the point. Tangent vectors are elements of this space.

Let’s make this super clear! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt

def f(x):
    return x**2

def tangent_line(x0, x):
    y0 = f(x0)
    slope = 2 * x0
    return slope * (x - x0) + y0

x = np.linspace(-2, 2, 100)
y = f(x)

x0 = 1
y0 = f(x0)

plt.plot(x, y, label='f(x) = x^2')
plt.plot(x, tangent_line(x0, x), '--', label=f'Tangent at x={x0}')
plt.plot(x0, y0, 'ro')
plt.legend()
plt.grid(True)
plt.show()

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🔥 Level up: Once you master this, you’ll be solving problems like a pro! Curvature of Plane Curves - Made Simple!

Curvature measures how quickly a curve changes direction. For a plane curve, it’s defined as the rate of change of the tangent vector with respect to arc length.

Let me walk you through this step by step! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt

def circle(t, r=1):
    return r * np.cos(t), r * np.sin(t)

def curvature(t, r=1):
    return 1 / r

t = np.linspace(0, 2*np.pi, 100)
x, y = circle(t)
k = curvature(t)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

ax1.plot(x, y)
ax1.set_aspect('equal')
ax1.set_title('Circle')

ax2.plot(t, k)
ax2.set_title('Curvature')

plt.tight_layout()
plt.show()

🚀 Frenet-Serret Frame - Made Simple!

The Frenet-Serret frame is a moving reference frame of three orthonormal vectors used to describe a curve locally at each point. It consists of the tangent, normal, and binormal unit vectors.

Let’s break this down together! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def helix(t):
    x = np.cos(t)
    y = np.sin(t)
    z = t / (2 * np.pi)
    return np.array([x, y, z])

def frenet_frame(t):
    r = helix(t)
    T = np.array([-np.sin(t), np.cos(t), 1 / (2 * np.pi)])
    T /= np.linalg.norm(T)
    B = np.array([-np.cos(t), -np.sin(t), 0])
    N = np.cross(B, T)
    return r, T, N, B

t = np.linspace(0, 4 * np.pi, 100)
points = np.array([helix(ti) for ti in t])

fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')

ax.plot(*points.T)

for ti in np.linspace(0, 4 * np.pi, 10):
    r, T, N, B = frenet_frame(ti)
    ax.quiver(*r, *T, color='r', length=0.5)
    ax.quiver(*r, *N, color='g', length=0.5)
    ax.quiver(*r, *B, color='b', length=0.5)

plt.show()

🚀 Surfaces and Parametrizations - Made Simple!

A surface is a two-dimensional manifold embedded in three-dimensional space. Parametrizations allow us to describe surfaces using two parameters.

Let’s make this super clear! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def torus(u, v, R=2, r=1):
    x = (R + r * np.cos(v)) * np.cos(u)
    y = (R + r * np.cos(v)) * np.sin(u)
    z = r * np.sin(v)
    return x, y, z

u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, 2 * np.pi, 100)
u, v = np.meshgrid(u, v)
x, y, z = torus(u, v)

fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x, y, z, cmap='viridis')
plt.show()

🚀 First Fundamental Form - Made Simple!

The first fundamental form is a quadratic form that allows measurements on a surface (such as length and angle) to be determined without reference to the ambient space.

Let’s make this super clear! Here’s how we can tackle this:

import numpy as np
import sympy as sp

# Define symbolic variables
u, v = sp.symbols('u v')

# Define a parametrization of a sphere
x = sp.sin(u) * sp.cos(v)
y = sp.sin(u) * sp.sin(v)
z = sp.cos(u)

# Compute partial derivatives
xu = sp.diff(x, u)
xv = sp.diff(x, v)
yu = sp.diff(y, u)
yv = sp.diff(y, v)
zu = sp.diff(z, u)
zv = sp.diff(z, v)

# Compute coefficients of the first fundamental form
E = xu**2 + yu**2 + zu**2
F = xu*xv + yu*yv + zu*zv
G = xv**2 + yv**2 + zv**2

print("First Fundamental Form:")
print(f"E = {E}")
print(f"F = {F}")
print(f"G = {G}")

🚀 Gaussian Curvature - Made Simple!

Gaussian curvature is an intrinsic measure of curvature that depends on how the surface is curved, without reference to an embedding space.

This next part is really neat! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def gaussian_curvature(x, y):
    return -2 / (1 + x**2 + y**2)**2

x = np.linspace(-2, 2, 100)
y = np.linspace(-2, 2, 100)
X, Y = np.meshgrid(x, y)
Z = gaussian_curvature(X, Y)

fig = plt.figure(figsize=(12, 5))

ax1 = fig.add_subplot(121, projection='3d')
ax1.plot_surface(X, Y, Z, cmap='viridis')
ax1.set_title('Gaussian Curvature')

ax2 = fig.add_subplot(122)
im = ax2.imshow(Z, extent=[-2, 2, -2, 2], origin='lower', cmap='viridis')
plt.colorbar(im)
ax2.set_title('Gaussian Curvature (Top View)')

plt.tight_layout()
plt.show()

🚀 Mean Curvature - Made Simple!

Mean curvature is an extrinsic measure of curvature that depends on the embedding of the surface in three-dimensional space. It’s the average of the principal curvatures.

Let me walk you through this step by step! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def mean_curvature(x, y):
    return 2 * (1 + x**2 + y**2) / (1 + x**2 + y**2)**2

x = np.linspace(-2, 2, 100)
y = np.linspace(-2, 2, 100)
X, Y = np.meshgrid(x, y)
Z = mean_curvature(X, Y)

fig = plt.figure(figsize=(12, 5))

ax1 = fig.add_subplot(121, projection='3d')
ax1.plot_surface(X, Y, Z, cmap='viridis')
ax1.set_title('Mean Curvature')

ax2 = fig.add_subplot(122)
im = ax2.imshow(Z, extent=[-2, 2, -2, 2], origin='lower', cmap='viridis')
plt.colorbar(im)
ax2.set_title('Mean Curvature (Top View)')

plt.tight_layout()
plt.show()

🚀 Geodesics - Made Simple!

Geodesics are curves on a surface that locally minimize the distance between points. They are the generalization of straight lines to curved spaces.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def sphere(u, v):
    x = np.sin(u) * np.cos(v)
    y = np.sin(u) * np.sin(v)
    z = np.cos(u)
    return x, y, z

def geodesic(t):
    return np.pi/2, t

u = np.linspace(0, np.pi, 100)
v = np.linspace(0, 2*np.pi, 100)
U, V = np.meshgrid(u, v)
X, Y, Z = sphere(U, V)

t = np.linspace(0, 2*np.pi, 100)
u_geo, v_geo = geodesic(t)
X_geo, Y_geo, Z_geo = sphere(u_geo, v_geo)

fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, alpha=0.3)
ax.plot(X_geo, Y_geo, Z_geo, color='r', linewidth=3)
plt.show()

🚀 Parallel Transport - Made Simple!

Parallel transport is a way of transporting geometric data along a curve on a manifold. It preserves the inner product of vectors and is used to compare vectors at different points on the manifold.

Let’s make this super clear! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def sphere(u, v):
    x = np.sin(u) * np.cos(v)
    y = np.sin(u) * np.sin(v)
    z = np.cos(u)
    return x, y, z

def parallel_transport(t, v0):
    u = np.pi/2
    x = np.cos(t)
    y = np.sin(t)
    z = 0
    T = np.array([-y, x, 0])
    N = np.array([0, 0, 1])
    B = np.cross(T, N)
    return x*v0[0]*T + y*v0[0]*B + v0[1]*N

u = np.linspace(0, np.pi, 100)
v = np.linspace(0, 2*np.pi, 100)
U, V = np.meshgrid(u, v)
X, Y, Z = sphere(U, V)

t = np.linspace(0, 2*np.pi, 20)
v0 = np.array([1, 0])
vectors = [parallel_transport(ti, v0) for ti in t]
points = [sphere(np.pi/2, ti) for ti in t]

fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, alpha=0.3)

for point, vector in zip(points, vectors):
    ax.quiver(*point, *vector, color='r', length=0.2)

plt.show()

🚀 Riemannian Manifolds - Made Simple!

A Riemannian manifold is a smooth manifold equipped with an inner product on the tangent space at each point, which varies smoothly from point to point. This inner product is called the Riemannian metric.

Ready for some cool stuff? Here’s how we can tackle this:

import numpy as np
import sympy as sp

# Define symbolic variables
x, y = sp.symbols('x y')

# Define a Riemannian metric on R^2
g11 = 1 + x**2
g12 = g21 = x*y
g22 = 1 + y**2

# Create the metric tensor
g = sp.Matrix([[g11, g12], [g21, g22]])

# Compute the determinant and inverse of the metric
det_g = g.det()
g_inv = g.inv()

print("Metric tensor:")
print(g)
print("\nDeterminant of metric:")
print(det_g)
print("\nInverse metric:")
print(g_inv)

# Compute Christoffel symbols (first kind)
christoffel = [[[sp.diff(g[i,j], x_k) + sp.diff(g[i,k], x_j) - sp.diff(g[j,k], x_i) 
                 for k in range(2)] for j in range(2)] for i in range(2)]

print("\nChristoffel symbols (first kind):")
for i in range(2):
    for j in range(2):
        for k in range(2):
            print(f"Γ_{i+1}{j+1}{k+1} = {christoffel[i][j][k]}")

🚀 Applications in Physics and Engineering - Made Simple!

Differential geometry has numerous applications in physics and engineering, including:

1. General Relativity: Describes gravity as the curvature of spacetime.
2. Fluid Dynamics: Models the flow of fluids on curved surfaces.
3. Computer Graphics: Used in 3D modeling and animation.
4. Robotics: Helps in path planning and control of robotic arms.

Ready for some cool stuff? Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def schwarzschild_metric(r, theta):
    Rs = 1  # Schwarzschild radius
    g00 = -(1 - Rs / r)
    g11 = 1 / (1 - Rs / r)
    g22 = r**2
    g33 = r**2 * np.sin(theta)**2
    return np.diag([g00, g11, g22, g33])

r = np.linspace(1.1, 5, 100)
theta = np.linspace(0, np.pi, 100)
R, THETA = np.meshgrid(r, theta)

g00 = schwarzschild_metric(R, THETA)[0, 0]

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(R * np.sin(THETA), R * np.cos(THETA), g00, cmap='viridis')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('g00')
ax.set_title('Schwarzschild Metric Component g00')
plt.colorbar(surf)
plt.show()

🚀 Lie Groups and Lie Algebras - Made Simple!

Lie groups are smooth manifolds that are also groups, with smooth group operations. Lie algebras are the tangent spaces at the identity of Lie groups, equipped with a bracket operation.

Let me walk you through this step by step! Here’s how we can tackle this:

import numpy as np
import matplotlib.pyplot as plt

def rotation_matrix(theta):
    return np.array([[np.cos(theta), -np.sin(theta)],
                     [np.sin(theta), np.cos(theta)]])

def plot_so2():
    thetas = np.linspace(0, 2*np.pi, 100)
    x = np.cos(thetas)
    y = np.sin(thetas)
    
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
    
    # Plot SO(2)
    ax1.plot(x, y)
    ax1.set_aspect('equal')
    ax1.set_title('SO(2) - Special Orthogonal Group')
    ax1.set_xlabel('cos(θ)')
    ax1.set_ylabel('sin(θ)')
    
    # Plot action on a point
    point = np.array([1, 0])
    rotated_points = np.array([rotation_matrix(theta) @ point for theta in thetas])
    
    ax2.plot(rotated_points[:, 0], rotated_points[:, 1])
    ax2.set_aspect('equal')
    ax2.set_title('Action of SO(2) on (1, 0)')
    ax2.set_xlabel('x')
    ax2.set_ylabel('y')
    
    plt.tight_layout()
    plt.show()

plot_so2()

🚀 Connections and Covariant Derivatives - Made Simple!

Connections provide a way to compare vectors at different points on a manifold. The covariant derivative is a generalization of the directional derivative to manifolds, using a connection.

Let’s make this super clear! Here’s how we can tackle this:

import sympy as sp

# Define symbolic variables and metric
x, y = sp.symbols('x y')
g = sp.Matrix([[1 + x**2, x*y], [x*y, 1 + y**2]])

# Compute Christoffel symbols
def christoffel(g, coord):
    n = g.shape[0]
    gamma = [[[0 for _ in range(n)] for _ in range(n)] for _ in range(n)]
    g_inv = g.inv()
    for i in range(n):
        for j in range(n):
            for k in range(n):
                gamma[i][j][k] = sum(g_inv[i, l] * (g[l, j].diff(coord[k]) + 
                                                    g[l, k].diff(coord[j]) - 
                                                    g[j, k].diff(coord[l])) 
                                     for l in range(n)) / 2
    return gamma

gamma = christoffel(g, [x, y])

print("Christoffel symbols:")
for i in range(2):
    for j in range(2):
        for k in range(2):
            print(f"Γ^{i}_{j}{k} = {gamma[i][j][k]}")

🚀 Additional Resources - Made Simple!

For further exploration of Differential Geometry, consider these resources:

1. ArXiv.org: “Introduction to Differential Geometry” by John M. Lee URL: https://arxiv.org/abs/math/9805030
2. ArXiv.org: “Notes on Differential Geometry and Lie Groups” by Jean Gallier URL: https://arxiv.org/abs/1805.01462
3. ArXiv.org: “Differential Geometry of Curves and Surfaces” by Manfredo P. do Carmo URL: https://arxiv.org/abs/math/0406005

These resources provide in-depth coverage of the topics we’ve introduced in this slideshow. Remember to verify the availability and content of these papers, as ArXiv listings may change over time.

🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀