Physics Simulation with Python is a broad scope of topics from conventional mechanics to electromagnetism and quantum physics. We work on all areas and are updated with best project ideas and topics that are tailored to your requirements so send us a message to get your work done at right time. Encompassing instance code snippets and descriptions, we offer some extensive project topics for physics simulations with Python:

__Classical Mechanics__

**Projectile Motion Simulation**

- Under gravity and resistance of air, we plan to simulate the path of a projectile.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

v0 = 50 # initial velocity (m/s)

theta = 45 # launch angle (degrees)

g = 9.81 # acceleration due to gravity (m/s^2)

dt = 0.01 # time step (s)

# Initial conditions

theta_rad = np.radians(theta)

vx = v0 * np.cos(theta_rad)

vy = v0 * np.sin(theta_rad)

x, y = 0, 0

# Lists to store the results

x_data, y_data = [x], [y]

# Simulation loop

while y >= 0:

x += vx * dt

vy -= g * dt

y += vy * dt

x_data.append(x)

y_data.append(y)

# Plotting the trajectory

plt.plot(x_data, y_data)

plt.xlabel(‘Horizontal Distance (m)’)

plt.ylabel(‘Vertical Distance (m)’)

plt.title(‘Projectile Motion’)

plt.grid(True)

plt.show()

**Simple Harmonic Motion**

- A mass-spring framework ought to be simulated. Our team aims to visualize its oscillatory movement.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

k = 10 # spring constant (N/m)

m = 1 # mass (kg)

x0 = 1 # initial displacement (m)

v0 = 0 # initial velocity (m/s)

dt = 0.01 # time step (s)

t_max = 20 # total simulation time (s)

# Initial conditions

x = x0

v = v0

# Lists to store the results

t_data = np.arange(0, t_max, dt)

x_data = []

# Simulation loop

for t in t_data:

a = -k/m * x # acceleration

v += a * dt

x += v * dt

x_data.append(x)

# Plotting the displacement over time

plt.plot(t_data, x_data)

plt.xlabel(‘Time (s)’)

plt.ylabel(‘Displacement (m)’)

plt.title(‘Simple Harmonic Motion’)

plt.grid(True)

plt.show()

**Pendulum Simulation**

- A fundamental pendulum has to be simulated. Mainly, we focus on examining its movement.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

g = 9.81 # acceleration due to gravity (m/s^2)

L = 1 # length of the pendulum (m)

theta0 = np.pi / 4 # initial angle (radians)

omega0 = 0 # initial angular velocity (rad/s)

dt = 0.01 # time step (s)

t_max = 10 # total simulation time (s)

# Time array

t = np.arange(0, t_max, dt)

# Arrays to store the results

theta = np.zeros_like(t)

omega = np.zeros_like(t)

theta[0] = theta0

omega[0] = omega0

# Function to calculate the derivatives

def derivatives(theta, omega, g, L):

dtheta_dt = omega

domega_dt = -(g / L) * np.sin(theta)

return dtheta_dt, domega_dt

# Runge-Kutta 4th order method

for i in range(1, len(t)):

k1_theta, k1_omega = derivatives(theta[i-1], omega[i-1], g, L)

k2_theta, k2_omega = derivatives(theta[i-1] + 0.5*dt*k1_theta, omega[i-1] + 0.5*dt*k1_omega, g, L)

k3_theta, k3_omega = derivatives(theta[i-1] + 0.5*dt*k2_theta, omega[i-1] + 0.5*dt*k2_omega, g, L)

k4_theta, k4_omega = derivatives(theta[i-1] + dt*k3_theta, omega[i-1] + dt*k3_omega, g, L)

theta[i] = theta[i-1] + (dt/6.0)*(k1_theta + 2*k2_theta + 2*k3_theta + k4_theta)

omega[i] = omega[i-1] + (dt/6.0)*(k1_omega + 2*k2_omega + 2*k3_omega + k4_omega)

# Plotting the results

plt.plot(t, theta, label=’Theta (Angle)’)

plt.plot(t, omega, label=’Omega (Angular Velocity)’)

plt.xlabel(‘Time (s)’)

plt.ylabel(‘Values’)

plt.title(‘Pendulum Simulation’)

plt.legend()

plt.grid(True)

plt.show()

__Electromagnetism__

**Electric Field of a Point Charge**

- The electric field must be visualized which is produced by a point charge.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

q = 1 # charge (C)

k = 8.99e9 # Coulomb’s constant (N m^2/C^2)

x_range = np.linspace(-10, 10, 400)

y_range = np.linspace(-10, 10, 400)

X, Y = np.meshgrid(x_range, y_range)

# Electric field components

Ex = k * q * X / (X**2 + Y**2)**(3/2)

Ey = k * q * Y / (X**2 + Y**2)**(3/2)

# Plotting the electric field

plt.figure(figsize=(8, 8))

plt.quiver(X, Y, Ex, Ey)

plt.title(‘Electric Field of a Point Charge’)

plt.xlabel(‘x (m)’)

plt.ylabel(‘y (m)’)

plt.grid(True)

plt.show()

**Magnetic Field of a Current-Carrying Wire**

- The magnetic field across a straight current-carrying wire should be simulated.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

I = 1 # current (A)

mu_0 = 4 * np.pi * 1e-7 # permeability of free space (T m/A)

x_range = np.linspace(-10, 10, 400)

y_range = np.linspace(-10, 10, 400)

X, Y = np.meshgrid(x_range, y_range)

# Magnetic field components

Bx = -mu_0 * I * Y / (2 * np.pi * (X**2 + Y**2))

By = mu_0 * I * X / (2 * np.pi * (X**2 + Y**2))

# Plotting the magnetic field

plt.figure(figsize=(8, 8))

plt.quiver(X, Y, Bx, By)

plt.title(‘Magnetic Field of a Current-Carrying Wire’)

plt.xlabel(‘x (m)’)

plt.ylabel(‘y (m)’)

plt.grid(True)

plt.show()

**Electromagnetic Wave Propagation**

- In free space, we intend to simulate the diffusion of an electromagnetic wave.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

c = 3e8 # speed of light (m/s)

wavelength = 1 # wavelength (m)

k = 2 * np.pi / wavelength # wave number (rad/m)

omega = k * c # angular frequency (rad/s)

t = 0 # initial time (s)

dt = 0.01 # time step (s)

x = np.linspace(0, 10, 1000) # spatial domain

# Function to calculate the electric and magnetic fields

def fields(x, t):

E = np.sin(k * x – omega * t)

B = np.sin(k * x – omega * t)

return E, B

# Plotting the fields over time

fig, ax = plt.subplots(figsize=(10, 6))

for i in range(100):

E, B = fields(x, t)

ax.clear()

ax.plot(x, E, label=’Electric Field (E)’)

ax.plot(x, B, label=’Magnetic Field (B)’)

ax.set_xlabel(‘Position (m)’)

ax.set_ylabel(‘Field Amplitude’)

ax.set_title(‘Electromagnetic Wave Propagation’)

ax.legend()

plt.pause(0.1)

t += dt

__Thermodynamics__

**Ideal Gas Simulation**

- In a container, our team aims to simulate the characteristics of an ideal gas. It is significant to validate the ideal gas law.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

n = 100 # number of particles

L = 10 # size of the container (m)

v_max = 1 # maximum initial speed (m/s)

dt = 0.01 # time step (s)

t_max = 10 # total simulation time (s)

# Initialize particle positions and velocities

np.random.seed(0)

positions = np.random.rand(n, 2) * L

velocities = (np.random.rand(n, 2) – 0.5) * v_max * 2

# Simulation loop

for _ in range(int(t_max / dt)):

positions += velocities * dt

# Reflect particles off the walls

velocities[positions[:, 0] < 0] *= [-1, 1]

velocities[positions[:, 0] > L] *= [-1, 1]

velocities[positions[:, 1] < 0] *= [1, -1]

velocities[positions[:, 1] > L] *= [1, -1]

# Plotting the final positions

plt.figure(figsize=(8, 8))

plt.scatter(positions[:, 0], positions[:, 1], s=10)

plt.xlim(0, L)

plt.ylim(0, L)

plt.xlabel(‘x (m)’)

plt.ylabel(‘y (m)’)

plt.title(‘Ideal Gas Simulation’)

plt.grid(True)

plt.show()

**Heat Diffusion in a Rod**

- Along a 1D (One -Dimensional) rod, the dispersion of heat must be simulated.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

L = 10 # length of the rod (m)

T_left = 100 # temperature at the left end (C)

T_right = 0 # temperature at the right end (C)

alpha = 0.01 # thermal diffusivity (m^2/s)

dx = 0.1 # spatial step size (m)

dt = 0.01 # time step (s)

t_max = 2 # total simulation time (s)

# Discretize the rod

x = np.arange(0, L + dx, dx)

T = np.zeros_like(x)

T[0] = T_left

T[-1] = T_right

# Simulation loop

for _ in range(int(t_max / dt)):

T_new = T.copy()

for i in range(1, len(x) – 1):

T_new[i] = T[i] + alpha * dt / dx**2 * (T[i+1] – 2*T[i] + T[i-1])

T = T_new

# Plotting the temperature distribution

plt.plot(x, T)

plt.xlabel(‘Position (m)’)

plt.ylabel(‘Temperature (C)’)

plt.title(‘Heat Diffusion in a Rod’)

plt.grid(True)

plt.show()

__Quantum Mechanics__

**Particle in a Box**

- In a one-dimensional box, we focus on simulating the wavefunction of a particle.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

L = 10 # length of the box (m)

n = 1 # quantum number

x = np.linspace(0, L, 1000) # spatial domain

# Wavefunction

psi = np.sqrt(2 / L) * np.sin(n * np.pi * x / L)

# Plotting the wavefunction

plt.plot(x, psi)

plt.xlabel(‘Position (m)’)

plt.ylabel(‘Wavefunction (ψ)’)

plt.title(‘Particle in a Box (n=1)’)

plt.grid(True)

plt.show()

**Quantum Harmonic Oscillator**

- Generally, the wavefunctions of a quantum harmonic oscillator ought to be simulated.

import numpy as np

import matplotlib.pyplot as plt

from scipy.special import hermite

# Parameters

m = 1 # mass (kg)

omega = 1 # angular frequency (rad/s)

hbar = 1 # reduced Planck’s constant (J s)

n = 0 # quantum number

x = np.linspace(-5, 5, 1000) # spatial domain

# Wavefunction

Hn = hermite(n)

psi = (1 / np.sqrt(2**n * np.math.factorial(n))) * ((m * omega / (np.pi * hbar))**0.25) * np.exp(-m * omega * x**2 / (2 * hbar)) * Hn(np.sqrt(m * omega / hbar) * x)

# Plotting the wavefunction

plt.plot(x, psi)

plt.xlabel(‘Position (m)’)

plt.ylabel(‘Wavefunction (ψ)’)

plt.title(‘Quantum Harmonic Oscillator (n=0)’)

plt.grid(True)

plt.show()

__Advanced Projects__

**Double Pendulum Simulation**

- A double pendulum must be simulated. Typically, our team intends to investigate its disruptive features.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

g = 9.81 # acceleration due to gravity (m/s^2)

L1 = 1.0 # length of the first pendulum (m)

L2 = 1.0 # length of the second pendulum (m)

m1 = 1.0 # mass of the first pendulum (kg)

m2 = 1.0 # mass of the second pendulum (kg)

theta1_0 = np.pi / 2 # initial angle of the first pendulum (radians)

theta2_0 = np.pi / 4 # initial angle of the second pendulum (radians)

omega1_0 = 0.0 # initial angular velocity of the first pendulum (radians/s)

omega2_0 = 0.0 # initial angular velocity of the second pendulum (radians/s)

dt = 0.01 # time step (s)

t_max = 20 # total simulation time (s)

# Time array

t = np.arange(0, t_max, dt)

# Arrays to store the results

theta1 = np.zeros_like(t)

omega1 = np.zeros_like(t)

theta2 = np.zeros_like(t)

omega2 = np.zeros_like(t)

theta1[0] = theta1_0

omega1[0] = omega1_0

theta2[0] = theta2_0

omega2[0] = omega2_0

# Function to calculate the derivatives

def derivatives(theta1, omega1, theta2, omega2, g, L1, L2, m1, m2):

delta = theta2 – theta1

denom1 = (m1 + m2) * L1 – m2 * L1 * np.cos(delta) * np.cos(delta)

denom2 = (L2 / L1) * denom1

dtheta1_dt = omega1

dtheta2_dt = omega2

domega1_dt = (m2 * L1 * omega1 * omega1 * np.sin(delta) * np.cos(delta) +

m2 * g * np.sin(theta2) * np.cos(delta) +

m2 * L2 * omega2 * omega2 * np.sin(delta) –

(m1 + m2) * g * np.sin(theta1)) / denom1

domega2_dt = (-m2 * L2 * omega2 * omega2 * np.sin(delta) * np.cos(delta) +

(m1 + m2) * g * np.sin(theta1) * np.cos(delta) –

(m1 + m2) * L1 * omega1 * omega1 * np.sin(delta) –

(m1 + m2) * g * np.sin(theta2)) / denom2

return dtheta1_dt, domega1_dt, dtheta2_dt, domega2_dt

# Runge-Kutta 4th order method

for i in range(1, len(t)):

k1_theta1, k1_omega1, k1_theta2, k1_omega2 = derivatives(theta1[i-1], omega1[i-1], theta2[i-1], omega2[i-1], g, L1, L2, m1, m2)

k2_theta1, k2_omega1, k2_theta2, k2_omega2 = derivatives(theta1[i-1] + 0.5*dt*k1_theta1, omega1[i-1] + 0.5*dt*k1_omega1, theta2[i-1] + 0.5*dt*k1_theta2, omega2[i-1] + 0.5*dt*k1_omega2, g, L1, L2, m1, m2)

k3_theta1, k3_omega1, k3_theta2, k3_omega2 = derivatives(theta1[i-1] + 0.5*dt*k2_theta1, omega1[i-1] + 0.5*dt*k2_omega1, theta2[i-1] + 0.5*dt*k2_theta2, omega2[i-1] + 0.5*dt*k2_omega2, g, L1, L2, m1, m2)

k4_theta1, k4_omega1, k4_theta2, k4_omega2 = derivatives(theta1[i-1] + dt*k3_theta1, omega1[i-1] + dt*k3_omega1, theta2[i-1] + dt*k3_theta2, omega2[i-1] + dt*k3_omega2, g, L1, L2, m1, m2)

theta1[i] = theta1[i-1] + (dt/6.0)*(k1_theta1 + 2*k2_theta1 + 2*k3_theta1 + k4_theta1)

omega1[i] = omega1[i-1] + (dt/6.0)*(k1_omega1 + 2*k2_omega1 + 2*k3_omega1 + k4_omega1)

theta2[i] = theta2[i-1] + (dt/6.0)*(k1_theta2 + 2*k2_theta2 + 2*k3_theta2 + k4_theta2)

omega2[i] = omega2[i-1] + (dt/6.0)*(k1_omega2 + 2*k2_omega2 + 2*k3_omega2 + k4_omega2)

# Plotting the results

plt.figure(figsize=(12, 6))

plt.subplot(2, 1, 1)

plt.plot(t, theta1, label=’Theta1 (Angle of Pendulum 1)’)

plt.plot(t, theta2, label=’Theta2 (Angle of Pendulum 2)’)

plt.xlabel(‘Time (s)’)

plt.ylabel(‘Angle (rad)’)

plt.title(‘Double Pendulum Simulation – Angles’)

plt.legend()

plt.grid(True)

plt.subplot(2, 1, 2)

plt.plot(t, omega1, label=’Omega1 (Angular Velocity of Pendulum 1)’)

plt.plot(t, omega2, label=’Omega2 (Angular Velocity of Pendulum 2)’)

plt.xlabel(‘Time (s)’)

plt.ylabel(‘Angular Velocity (rad/s)’)

plt.title(‘Double Pendulum Simulation – Angular Velocities’)

plt.legend()

plt.grid(True)

plt.tight_layout()

plt.show()

**Solar System Simulation**

- Through the utilization of Newton’s law of motion and gravitation, we plan to simulate the movement of planets in the solar system.

import numpy as np

import matplotlib.pyplot as plt

# Parameters

G = 6.67430e-11 # gravitational constant (m^3 kg^-1 s^-2)

M_sun = 1.989e30 # mass of the Sun (kg)

dt = 1e4 # time step (s)

t_max = 3.154e7 # total simulation time (s)

# Initial conditions for Earth

r_earth = np.array([1.496e11, 0]) # initial position (m)

v_earth = np.array([0, 2.978e4]) # initial velocity (m/s)

# Arrays to store the results

positions = []

positions.append(r_earth.copy())

# Simulation loop

for _ in range(int(t_max / dt)):

r = np.linalg.norm(r_earth)

a = -G * M_sun / r**3 * r_earth

v_earth += a * dt

r_earth += v_earth * dt

positions.append(r_earth.copy())

# Convert results to arrays for plotting

positions = np.array(positions)

# Plotting the trajectory

plt.figure(figsize=(8, 8))

plt.plot(positions[:, 0], positions[:, 1])

plt.scatter(0, 0, color=’yellow’, label=’Sun’)

plt.xlabel(‘x (m)’)

plt.ylabel(‘y (m)’)

plt.title(‘Earth\’s Orbit around the Sun’)

plt.legend()

plt.grid(True)

plt.axis(‘equal’)

plt.show()

**physics simulation python Projects**

In the current years, numerous project topics on physics simulation are emerging continuously. Encompassing different regions like electromagnetism, quantum mechanics, classical mechanics, thermodynamics, and more, we suggest 50 extensive physics simulation project topics with Python:

__Classical Mechanics__

**Projectile Motion Simulation**

- Under the impact of gravity and resistance of air, we plan to simulate the path of a projectile. Generally, various positions, drag coefficients, and preliminary speeds ought to be examined.

**Simple Harmonic Oscillator**

- The movement of a mass-spring model has to be simulated. It is appreciable to investigate the impacts of various spring constants and masses.

**Damped Harmonic Oscillator**

- Our team aims to simulate a damped harmonic oscillator. Typically, the impacts of differing damping coefficients must be explored.

**Pendulum Simulation**

- We focus on designing the fundamental pendulum movement. Crucial impacts of angle of incidence and various lengths ought to be evaluated.

**Double Pendulum Simulation**

- Mainly, a double pendulum has to be simulated. We plan to examine its disruptive features in an appropriate manner.

**N-Body Gravitational Simulation**

- The gravitational communications among numerous bodies like moons and planets should be simulated.

**Circular Motion**

- An object ought to be simulated which is in consistent circular movement. We focus on examining the significant connection among centripetal force, velocity, and mass.

**Rotational Dynamics**

- The rotation of a rigid body must be designed. It is approachable to investigate rotational inertia, torque, and angular momentum.

**Collision Simulation**

- Among particles, our team focuses on simulating flexible and inflexible collisions. The maintenance of energy and momentum has to be examined.

**Projectile Motion with Drag**

- Focusing on air resistance, we intend to simulate movement of the projectile. Mainly, the outcomes have to be contrasted with perfect projectile movement.

__Electromagnetism__

**Electric Field of Point Charges**

- The electric field must be visualized which is produced by numerous point charges. It is advisable to investigate magnetic lines and superposition.

**Electric Potential of Point Charges**

- In the case of an arrangement of point charges, we focus on assessing and visualizing the electric potential.

**Capacitor Simulation**

- The characteristics of a parallel plate capacitor ought to be designed. It is appreciable to explore the crucial impacts of various dielectric resources and plate separations.

**Current-Carrying Wire Magnetic Field**

- The magnetic field has to be simulated, which is across a straight current-carrying wire. Our team aims to visualize the magnetic lines in an effective manner.

**Electromagnetic Wave Propagation**

- In free space, we plan to design the diffusion of an electromagnetic wave. Typically, the significant connection among magnetic and electric fields should be explored.

**RC Circuit Simulation**

- An RC circuit has to be simulated. It is advisable to examine the discharging and charging characteristics of the capacitor.

**RL Circuit Simulation**

- Our team aims to simulate an RL circuit. The temporary reaction of the inductor ought to be explored.

**RLC Circuit Simulation**

- Generally, an RLC circuit must be designed. We intend to investigate its damping characteristics and frequency of resonance.

**Induced EMF in a Moving Conductor**

- The induced electromotive force (EMF) in a conductor should be simulated which is travelling across a magnetic field.

**Faraday’s Law of Induction**

- In the case of a varying magnetic field, our team designs the creation of EMF in a coil. Generally, several rates of variation and coil features have to be investigated.

__Thermodynamics__

**Ideal Gas Law Simulation**

- In a container, we aim to design the characteristics of an ideal gas. Through differing pressure, temperature, and volume, it is advisable to validate the ideal gas law.

**Heat Transfer in a Rod**

- The heat transmission ought to be simulated, which is across a one-dimensional rod. Periodically, our team examines the temperature dispersion.

**Heat Transfer in a 2D Plate**

- In a two-dimensional plate, we focus on simulating the heat transmission. Typically, balanced and temporary heat transmission has to be investigated.

**Carnot Cycle Simulation**

- The Carnot cycle must be simulated. Our team plans to assess the Carnot engine effectiveness.

**Maxwell-Boltzmann Distribution**

- In an ideal gas, we visualize the Maxwell-Boltzmann dissemination of molecular speeds. Mainly, various temperatures must be examined.

**Isothermal and Adiabatic Processes**

- For an ideal gas, it is significant to simulate procedures of isothermal and adiabatic. In every procedure, our team focuses on contrasting the performed tasks and PV diagrams.

**Phase Change Simulation**

- With the support of latent heat and heat transmission policies, we intend to design the phase transition of a substance such as water to stream.

**Entropy Change in Thermodynamic Processes**

- For different thermodynamic procedures, our team assesses and visualizes the variation in entropy.

**Heat Engines and Efficiency**

- Typically, various heat engine cycles like Diesel, Otto have to be simulated. It is advisable to assess their capabilities.

**Thermal Expansion**

- The thermal expansion of solids should be designed. Among temperature variations and developments, focus on examining the associations or connections.

__Quantum Mechanics__

**Particle in a Box**

- Consider a particle which is enclosed in a one-dimensional box and simulate their wavefunction. For various energy levels, we visualize the probability density.

**Quantum Harmonic Oscillator**

- Specifically, it is approachable to design the wavefunctions of a quantum harmonic oscillator. Ground states and excited states are required to be examined efficiently.

**Double-Slit Experiment**

- The double-slit experimentation ought to be simulated. Our team plans to visualize the intervention trend of particles.

**Hydrogen Atom Wavefunctions**

- For the hydrogen atom, we visualize the radial probability distribution functions.

**Tunneling Effect**

- Across a possible obstacle, it is approachable to simulate quantum tunnelling of a particle. The reflection and transmission coefficients should be examined.

**Quantum Entanglement**

- The entangled quantum conditions ought to be designed. We plan to examine the destruction of Bell’s discrepancies.

**Spin-1/2 Particles**

- In a magnetic field, our team simulates the characteristics of spin-1/2 particles. The theory of assessment and superposition has to be examined.

**Time-Dependent Schrödinger Equation**

- Consider a particle in a potential well and resolve the time-dependent Schrödinger equation.

**Quantum Superposition**

- The superposition of wavefunctions must be designed. We examine the resultant probability densities in an effective manner.

**Fermions and Bosons**

- In a potential well, we simulate the characteristics of bosons and fermions. Mainly, the Bose-Einstein condensation and Pauli exclusion has to be investigated.

__Relativity__

**Time Dilation**

- Time dilation has to be simulated for objects that are traveling at the relativity speeds. We investigate the major connections among time and speed accomplished.

**Length Contraction**

- For objects that are traveling at the relativity speeds, length contraction must be designed. Among speed and examined length, our team investigates the crucial connection.

**Relativistic Momentum and Energy**

- Consider particles that are traveling at extreme speeds and assess their relativistic momentum and energy.

**Lorentz Transformations**

- On time and space scales, our team visualizes the impacts of Lorentz transformations.

**Relativistic Doppler Effect**

- For light and sound waves, we simulate the relativistic Doppler effect. Mainly, for various relative speeds, it is significant to investigate the Doppler effect.

__Miscellaneous__

**Fluid Dynamics Simulation**

- By means of employing the Navier-Stokes equations, our team aims to design flow of a fluid. The turbulent and laminar flow should be simulated.

**Wave Propagation in a Medium**

- In a medium, we simulate the diffusion of mechanical waves. Typically, diffraction, reflection, and refraction has to be investigated.

**Chaos Theory and Lorenz Attractor**

- The Lozenz attractor must be simulated. Our team intends to visualize its disruptive features.

**Solar System Simulation**

- Through the utilization of Newton’s laws of gravitation, we design the movement of moons and planets in the solar system.

**Crystallography and Bragg’s Law**

- With the aid of Bragg’s law, it is appreciable to simulate X-ray diffraction trends for crystal architectures.

Encompassing instance code snippets and outlines, we have provided several project topics for physics simulation. Also, 50 widespread physics simulation project topics with Python including numerous regions like quantum mechanics, electromagnetism, classical mechanics, and thermodynamics, and more are recommended by us in this article.