README.md

16.C21A/18.C21A Numerical Methods for Partial Differential Equations

This new (Spring 2026) MIT subject, part of MIT's Common Ground for Computing Education, introduces numerical methods and numerical analysis to a broad audience (assuming 18.03, 18.06, or equivalents, and some programming experience).  It is divided into two 6-unit halves:

• 16.C21/18.C21 Interdisciplinary Numerical Methods (first half-term “hub”): basic numerical methods, including curve fitting, root finding, numerical differentiation and integration, numerical differential equations, and floating-point arithmetic. Emphasizes the complementary concerns of accuracy and computational cost. Prof. Steven G. Johnson and Prof. Youssef Marzouk.

• Second half-term: two options for 6-unit “spokes”

  • 16.C21A/18.C21A, Numerical methods for partial differential equations: finite difference, finite volume, and finite element methods; boundary conditions, accuracy, and stability. Prof. Youssef Marzouk.

  • 16.C21B/18.C21Bm Large-scale linear algebra: sparse matrices, iterative methods, randomized methods. Prof. Steven G. Johnson.

Taking both the hub and any spoke will count as an 18.3xx class for math majors, similar to 18.330, and as 16.90 for AeroAstro majors.

This repository holds materials for 16.C21A/18.C21A.

16.C21A/18.C21A Syllabus, Spring 2026

Instructors: Prof. Youssef Marzouk and TA Andrey Bryutkin.

Lectures: MWF10 in 45-102 (Mar 30–May 11). 6 units. Prerequisite is 16.C21/18.C21 or permission of instructor.

Assignments:

• 4 weekly problem sets, due Wednesdays at 11:59 pm (April 8, 15, 22, 29). You will have oral check-ins on 3 psets (randomly selected) where you have to explain your work to the TA or instructor (pass/fail per problem).

• An individual final project, in which you will implement and explore a method for numerical solution of PDEs that goes beyond what was previously covered in class. (This could involve a more sophisticated numerical method and/or a different class of PDEs.) The project will comprise both an oral presentation to the class (May 6, 8, or 11) and a written report (due May 12).

• No in-class exams and no final exam!

Assignment logistics:

• Problem set collaboration policy: Talk to anyone you want to and read anything you want to, with two caveats: First, make a solid effort to solve a problem on your own before discussing it with classmates or googling. Second, no matter whom you talk to or what you read, write up the solution on your own, without having their answer in front of you (this includes ChatGPT and similar). (You can use psetpartners.mit.edu to find problem set partners.)

• Homework assignments will require some programming. You can use either Julia or Python (your choice; instruction and examples will mostly use Julia).

• Submit your homework electronically via Gradescope on Canvas as a PDF containing code and results (e.g., from a Jupyter notebook) and a scan of any handwritten solutions.

• For accommodations, speak with S3 and have them contact the instructors.

Grading: 40% psets + 25% check-ins + 30% final project + 5% lecture attendance.

Office hours: Prof. Marzouk: Mondays 4-5 pm in 32-D714 (starting April 6). Prof. Bryutkin: Wednesdays 4-5 pm in 2-231A.

Resources: Piazza discussion forum, math learning center, TSR^2 study/resource room, pset partners.

Textbook: No required textbook. We will generally follow pdf course notes that will be posted below, along with suggestions for further reading following each lecture. Also, the book Fundamentals of Numerical Computation (FNC) by Driscoll and Braun is freely available online, has examples in Julia, Python, and Matlab, and is a valuable extra resource.

Lecture 1 (Mar 31)

• 16.C21A/18.C21A overview and syllabus: slides

We talked about the broad and rough categorization of PDEs into equations of elliptic, parabolic, and hyperbolic type. Then we presented certain canonical PDEs and their analytical solutions in simple settings, with a focus on intuitively understanding the behavior that these equations encode: (1) the advection equation $u_t - a u_x$, and (2) the diffusion equation $u_t = \mu u_{xx}$ (for $\mu > 0$), along with their multi-dimensional ($x \in \mathbb{R}^d$ for $d > 1$) analogs. We discussed the notions of initial condition and boundary conditions, and discussed roughly what kinds of boundary conditions make sense to impose in different situations.

Lecture 2 (Apr 1)

• Notes on finite difference methods: Chapter 8
• Problem Set #1, due on Wednesday April 8

We focused on advection-diffusion equations in one spatial dimension, and discussed how to build a semi-discrete approximation by substituting finite difference approximations of the spatial derivatives. The resulting ODE system can be solved by a variety of means; this approach (build a semi-discrete system and then apply an numerical ODE integration scheme) is generally known as the method of lines. Many—but not all—finite difference methods for PDEs can be constructed this way.

We also talked about simple boundary conditions, specifically Dirichlet and Neumann conditions, first in the continuous setting and then their discrete numerical implementations.

Lecture 3 (Apr 3)

• Notes on finite difference methods: Chapter 8

We talked about how to implement Neumann boundary conditions in a finite different scheme. Then we discussed the notion of local truncation error of a finite difference PDE discretization (and how this differs subtly from the notion of local truncation error we considered for ODE discretization), and derived local truncation error for a forward-time central-space discretization of our running-example advection-diffusion PDE. Then we did tried integrating this system in a simple code and found, in practice, major instabilities!

Lecture 4 (April 6)

• Notes on finite difference methods: Chapter 9
• Julia notebook examples: this and that