Potential Theory studies harmonic functions, potentials, and their boundary behavior, linking PDEs, complex analysis, and physics. Learners will gain intuition for Green's functions, the Laplace equation, and classical techniques for solving boundary value problems.
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30 minTypical time
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3Prerequisites
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4Learning outcomes
๐Prerequisites
Multivariable Calculus
Ordinary & Partial Differential Equations
Real Analysis (measure & integration basics)
๐ฏWhat You'll Learn
โAnalyze harmonic functions and characterize solutions to the Laplace equation
โConstruct and apply Green's functions and potentials for boundary value problems
โRelate potential-theoretic ideas to electrostatics and complex analysis
โApply maximum principles, Harnack inequalities, and capacity concepts
An introductory overview of potential theory, its core ideas, and how potentials relate to harmonic functions.
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Overview of Potential Theory: Potential theory studies harmonic and subharmonic functions, Newtonian potentials, and the behavior of physical fields such as gravity and electrostatics. It connects to complex analysis, probability (via Brownian motion), and partial differential equations. Core ideas include harmonic functions, Laplace's equation, Green's functions, and Poisson integrals, with applications to solving boundary value problems and understanding concepts like capacity and equilibrium measures.
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Field lines show how potential decreases radially from the charge
Equipotential contours reveal the dipole's field structure
Gradient direction indicates increasing potential
Arrows visualize field direction and relative magnitude
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Mathematical Foundations and Equations
Key equations, identities, and mathematical tools underpinning potential theory.
โฑ๏ธ15 min
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Core equations in potential theory include the Laplace equation โยฒฯ = 0, which characterizes harmonic potentials in charge-free regions, and Poisson's equation โยฒฯ = โฯ/ฮตโ, which relates the potential to a charge distribution ฯ. The fundamental solution in three dimensions is G(x,y) = 1/(4ฯ|xโy|), leading to the Newtonian potential ฯ(x) = โซ G(x,y)ฯ(y) dy for appropriate domains. Boundary value problems such as Dirichlet and Neumann problems specify the potential or its normal derivative on the boundary and are often solved via Green's identities and boundary integral representations. Green's formula expresses ฯ(x) in terms of boundary data: ฯ(x) = โฎ G(x,y) โn'ฯ(y) โ ฯ(y) โn'G(x,y) dS(y). The maximum principle states that a harmonic function attains its extrema on the boundary, while energy minimization principles (e.g., minimizing โซ|โฯ|ยฒ dV subject to boundary conditions) characterize physically relevant solutions. Gauss's law โยทE = ฯ/ฮตโ links the electric field to charge density, and with E = โโฯ it ties the potential to observable fields. In potential theory, these equations underpin boundary value problems and the representation of solutions via Green's functions.
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Mathematical Formula
Variables:
Type: mathematical
Example: Laplace equation in a domain
import numpy as np\n\ndef laplace_jacobi(u, max_iter=1000, tol=1e-6):\n nx, ny = u.shape\n un = u.copy()\n for it in range(max_iter):\n un[1:-1,1:-1] = 0.25*(u[:-2,1:-1] + u[2:,1:-1] + u[1:-1,:-2] + u[1:-1,2:])\n err = np.linalg.norm(un - u)\n u, un = un, u\n if err < tol:\n break\n return u\n\ndef example_domain_laplace(nx=50, ny=50):\n u = np.zeros((nx, ny))\n u[0, :] = 0\n u[-1, :] = 0\n u[:, 0] = 0\n u[:, -1] = 0\n u[nx//2, ny//2] = 1.0\n u = laplace_jacobi(u, max_iter=5000, tol=1e-5)\n return u\n\nif __name__ == '__main__':\n import matplotlib.pyplot as plt\n U = example_domain_laplace(100, 100)\n plt.imshow(U, origin='lower', cmap='viridis')\n plt.colorbar()\n plt.title('2D Laplace equation solution (Jacobi)')\n plt.show()
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Applications of Potential Theory
Where potential theory is used: physics, engineering, and mathematics.
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Real-world applications span diverse domains, from healthcare and finance to logistics and education. By translating data-driven insights into actionable steps, these applications improve decision-making, automate routine tasks, enhance user experiences, and enable scalable solutions in complex environments.
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Key application areas
Streamlines showing flow around a central charge
Equipotential contours overlaid on velocity field
3D slice showing gradient-driven flow
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Limitations, Pitfalls, and Open Questions
Important caveats when applying potential theory and areas for further research.
โฑ๏ธ12 min
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Limitations and caveats
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Historical Context and Further Reading
Key historical development and resources for deeper study.