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18.336 - Fast Methods for Partial Differential and Integral Equations
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18.336J/6.335J: Fast Methods for Partial Differential and Integral Equations

A unified introduction to the theory and practice of modern, near linear-time, numerical methods for large-scale partial differential and integral equations. Topics include: preconditioned iterative methods; generalized Fast Fourier Transform and other butterfly-based methods; multiresolution approaches including multigrid algorithms, hierarchical low-rank matrix decompositions, and low and high frequency Fast Multipole Methods. Example applications include: aircraft design, cardiovascular system modeling, electronic structure computation, and tomographic imaging.

This semester we will focus in particular in Fourier and polynomial spectral methods, FMM, methods for integral equations, and applications to fluid dynamics and electromagnetism.


Lectures: Tuesday/Thursday 9:30-11:00 am (2-136).

Office Hours: By appointment.

Prerequisites: This course covers advanced techniques for discretizing and solving PDEs. Some familiarity with ordinary differential equations, partial differential equaitons, Fourier transforms, linear algebra, and basic numerical methods for PDEs is assumed. It is strongly recommended that you have taken a previous course on basic numerical methods, such as 2.096/6.336/16.910, 2.097/6.339/16.920, 18.085, or 6.337/18.335. Problem sets will involve extensive coding and are required to be completed in Python or Julia notebooks.

Textbooks & Other Reading: Recommended reading will be posted as the class progresses. There is no textbook for the course, but the following books may be useful:

  • Strauss "Partial Differential Equations: An Introduction". An advanced undergrad intrdouction to PDEs.
  • Boyd "Chebyshev and Fourier Spectral Methods". Very readable and available online.
  • LeVeque "Finite difference methods for ordinary and partial differential equations".
  • LeVeque "Finite volume methods for hyperbolic problems".

Grading: 50% problem sets (approximately biweekly), 50% final project report and presentation.

Collaboration Policy: Make a strong effort to solve problems on your own before discussing with any classmates. You must write up your own code and solutions, and indicate your collaborators on your assignments.

Problem Sets

Problem Set 1: Implementing an FFT.

Available on Stellar. Due on Stellar by midnight on Tuesday 09/24.

Problem Set 2: Fourier and Finite Difference Helmholtz Solvers.

Available on Stellar. Due on Stellar by midnight on Thursday 10/17.

Problem Set 3: Chebyshev Spectral Solvers.

Available on Stellar. Due on Stellar by midnight on Thursday 11/14.

Problem Set 4: Low-Rank Methods.

Available on Stellar. Due on Stellar by midnight on Monday 12/09.

Final Projects

The final project is a 10-15 page paper and a 15 minute presentation during the last week of classes. The final project is broad in scope, but must include the implementation of a fast algorithm in Python or Julia along with performance and error analyses. The project can take the form a "lit. review project" discussing a published algorithm, or a "research project" attempting to implement a new solver for a problem of your choice.

Lit. review projects

One possibility is to review and implement an algorithm that was mentioned briefly or not covered in the course. Such a project should follow one or several published research papers describing the algorithm, along with a new implementation. Possible topics and suggested papers include:

Research projects

Another option is to use the methods covered in class to implement a fast solver for a research problem in your field. This should include:

  • A discussion of the scientific problem and a brief derivation of the model PDE.
  • Mention of the current commonly used methods in the field for the problem.
  • A fast implementation of a new solver for the problem, or a related first-step.
  • Discussion of the prospects and limitations of fast/high-order techniques for this problem in the future.

The goal should be producing a functional solver matching or improving on existing techniques in certain cases. However, it is understood that this may not turn out to be feasible (or even possible), and that's what makes it research! In that case, an implementation for a related toy model is expected, along with an analysis of the barriers to making a solver for the original problem.

Report & presentation format

Your report should be written in the style a SIAM article, using the SIAM article LaTeX templates. Your report should be between 10 and 15 pages. The SIAM layout is very spacious so this is not a lot of space, particularly if you have lots of images. Make sure your presentation and notation is concise!

Along with your report, you must submit the code implementing your algorithms, preferably in the form of a Jupyter notebook. Make good use of headings, text, comments, and descriptive function/variables names. Good code is code that is easily understandable by others!

Your report and presentation should both include:

  • Background information for your algorithm / physical application.
  • A concise mathematical description of the algorithm you're using.
  • Performance and error analysis of your implementation.

Lecture Material and Summaries

Lecture 1: Introduction to fast methods, PDEs, IEs


  • Course policies
  • Julia tutorial: 09/06/2019 5-7pm (32-141)
  • Why fast algorithms? History of fast algorithms for the Fourier transform.
  • Why PDEs? Models for physical systems. Classes of PDEs. Elliptic regularity theorem.
  • Why integral equations? Better conditioning from using exact solution formulae.

Related Reading

Lecture 2: Fast Fourier transforms


  • Continuous FT, discrete FT.
  • History of FFTs. Facts that make FFTs possible.
  • Radix-2 Cooley-Tukey algorithm.
  • Radix-3 and algorithms for prime N.

Related Reading

Lecture 3: Some applications of FFTs


  • Problem Set 1 assigned: due on Stellar by midnight on Tuesday 09/24.
  • Sine, cosine, and Chebyshev transforms.
  • Fast matrix-vector products using FFTs.
    • Circulant matrices: diagonalization using DFT.
    • Toeplitz matrices: embedding into circulant form.
    • Hankel matrices: converison to Toeplitz form.

Lecture 4: Finite differences and fast Poisson solvers in 1D


  • Review of finite difference methods.
  • Spectral preconditioning for fast Poisson solvers in 1D:
    • Periodic BCs using FFTs.
    • Dirichlet BCs using DSTs.
    • Neumann BCs using DCTs.
    • Gauge fixing.

Lecture 5: Fast finite difference solvers in multiple dimensions


  • Sylvester equations, Bartels-Stewart algorithm.
  • Kronecker products and block matrices.
  • Fast matrix-vector products with structured block matrices.
  • Fast Poisson solvers in multiple dimensions.
  • Extensions to other PDEs, e.g. Helmholtz.
  • Limitations and alternatives for non-constant coefficients.

Lecture 6: Domain decomposition methods


  • Extensions to other domains.
  • Schur complement / Poincare-Steklov method for domain decomposition.
    • Connecting two 1D segments.
    • Connecting two 2D boxes.
    • Heirarchical Poincare-Steklov method for multiple connections.
  • Distorted domains.

Related Reading

Lecture 7: Introduction to spectral methods


  • Wrap up domain decomposition and distorted domains. Comparison to finite elements.
  • Introduction to spectral methods.
    • Spectral representations of functions.
    • Rates of convergence.
    • Truncation and discretization error.
    • Convergence of Fourier series.

Related Reading

  • Boyd chapters 1 & 2.

Lecture 8: Fourier spectral methods I


  • Weighted residual method for discretizing PDEs.
  • Fourier solver for Poisson equation with periodic BCs.
    • Comparison to finite difference solver. Eigenvalue corrections.
  • Fourier spectral collocation.
  • Sine and cosine solvers for Poisson equation with Dirichlet/Neumann BCs.
    • Conditions on forcing for exponential convergence.

Related Reading

  • Boyd chapter 3.

Lecture 9: Fourier spectral methods II


  • Parity mixing.
  • Extending to multiple dimensions via direct products.
  • Extending to systems of equations. Maintaining bandedness.
  • Pseudospectral method for evaluating nonlinearities.

Related Reading

Lecture 10: Polynomial interpolation I


  • Wrap up: aliasing errors, dealiasing rules for arbitrary-order nonlinearities.
  • For more general equations and boundary conditions, need to use polynomials.
  • Polynomial interpolation. Lagrange representation.
  • Lebesgue constant of equispaced points. Runge phenomenon.
  • Lebesgue constant of Chebyshev nodes.

Lecture 11: Polynomial interpolation II


  • Convergence of Chebyshev series. Singularities in elliptical coordinates.
  • Gaussian quadrature. Deriving nodes and weights.
  • Discretization error from polynomial interpolation on Gaussian quadrature nodes.
  • Convergence of Gegenbauer polynomials. Legendre polynomials and finite elements.

Lecture 12: Chebyshev collocation methods


  • Collocation matrices for differentiation.
  • Collocation matrices for multiplication. Aliasing errors.
  • Roots vs extrema grid.
  • Boundary bordering. Generalized tau equivalence.

Lecture 13: Dense Chebyshev spectral methods


  • Rectangular collocation. Generalized tau equivalence.
  • Chebyshev recursion formulae.
  • T-to-T matrices for differentiation.
  • T-to-T matrices for multiplication. Band-limited expansions.

Related Reading

Lecture 14: Sparse Chebyshev spectral methods


  • Classical tau method for boundary conditions.
  • Chebyshev-T derivatives are Chebyshev-U polynomials.
  • T-to-U differentiation and conversion matrices.
  • Sparse T-to-U method. Generalized tau equivalence.
  • Sherman-Morrison formula for banded + bordered matrices.

Lecture 15: Ultraspherical method


  • Differentiation and conversion between ultraspherical polynomials.
  • Ultraspherical method for arbitrary-order linear ODEs. Generalized tau equivalence.
  • Woodbury matrix identity for banded + multi-bordered matrices.
  • Newton's method in function space for nonlinear ODEs.
  • Polynomial spectral methods in multiple dimensions:
    • Chebyshev x Chebyshev, alternating direction implicit scheme

Related Reading

Lecture 16: Multidimensional spectral methods


  • Fourier-Chebyshev multidimensional discretizations for periodic laters, cylinders, etc.
  • Pseudospectral method for nonlinear IVPs.
  • Block-banded formulation for systems of equations.
  • Example: incompressible Navier-Stokes without splitting.
  • Spectral methods with coordinate singularities:
    • Non-smoothness of vector/tensor components.
    • Radial regularity of Fourier components in polar coordinates.
    • Disk bases incorporating regularity.

Related Reading

Lecture 17: Introduction to low-rank methods


  • Scale separation in gravity.
    • Direct N-body using Newton's law for point masses.
    • Poisson equation for gravitational potential using Gauss's law for continuous distributions.
    • Both are limited in between: many point masses or well-separated distributions.
    • Green's function gives integral equation for potential: low rank for well-separated distributions.
  • Singular value decomposition.
    • Basic definition and derivation.
    • Optimal low-rank approximations using truncated SVDs.

Lecture 18: Approximating low-rank interactions


  • Fast matrix-vector products using low-rank approximations.
  • Low-rank approximations to functions. Fast inner products for integral equations.
  • Low-rank nature of gravitational interaction.
    • Projection: merging sources to compress the operator domain.
    • Interpolation: merging measurements to compress the operator range.
    • Duality of projection and interpolation.
  • Numerically approximating the SVD using Gaussian elimination for matrices and functions.

Lecture 19: Fast multipole methods


  • Analytical low-rank expansions using Taylor series.
  • 2D and 3D multipole expansions. Error estimates.
  • Hierarchical decompositions for error control.
  • Barnes-Hut and FMM in 2D. Comparison to direct methods.

Related Reading

Lecture 20: Boundary integral equations I


  • From PDEs to integral equations: complicated boundaries, exterior problems.
  • Basic ideas from potential theory for Laplace equation:
    • Fictitious charges.
    • Single-layer potentials.
    • Double-layer potentials.
  • Weakly singular kernels. Compact operators. First and second kind Fredholm integral equations.
  • Discritizing BIEs. Quadrature on panels.

Related Reading

Lecture 21: Boundary integral equations II


  • BIEs for other equations:
    • Stokes flow. Stokeslets and Stresslets.
    • Linear elasticity.
    • Helmholtz. Spurious resonances and combined field forms.
    • Time-harmonic Maxwell.
  • Other challenges:
    • Singular kernels.
    • Domains with corners. Diadic refinement.
  • Summary and comparison to PDE solvers.

Related Reading

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