2022-08-22 First Day#

CSCI-5636: Numerical solution of partial differential equations (PDE)#

Instructor: Jed Brown, jed.brown@colorado.edu, ECOT 824#

Meeting: Mon, Wed, Fri at 11:15-12:05 in ECCS 1B14 (and virtually)#

What are PDE?#

partial derivatives#

  • Gradient \(\mathbf g(\mathbf x) = \nabla u(\mathbf x)\)

    • scalar field to vector field

  • Curl \(\mathbf c(\mathbf x) = \nabla\times \mathbf g(\mathbf x)\)

  • vector field to vector field

  • Divergence \(d(\mathbf x) = \nabla\cdot \mathbf c(\mathbf x)\)

    • vector field to scalar field

  • time derivatives: \(\frac{\partial u(t, \mathbf x)}{\partial t}\)

what do they describe?#

  • heat transfer

  • electrostatics, electromagnetics

  • solid mechanics

  • fluid mechanics

  • quantum mechanics

  • population dynamics

Find \(u\) such that

(1)#\[\begin{align} -\nabla\cdot\big(\kappa(\mathbf x) \nabla u(\mathbf x)\big) &= f(\mathbf x) & \mathbf x &\in \Omega \\ u(\mathbf x) &= g(\mathbf x) & \mathbf x &\in \Gamma_D \subset \partial\Omega \\ \kappa(\mathbf x) \nabla u(\mathbf x) \cdot \mathbf{\hat n}(\mathbf x) &= h(\mathbf x) & \mathbf x &\in \Gamma_N \subset\partial\Omega \end{align}\]

Structural mechanics#

What does it mean to discretize a PDE?#

Find \(u\) such that

(2)#\[\begin{align} -\nabla\cdot\big(\kappa(\mathbf x) \nabla u(\mathbf x)\big) &= f(\mathbf x) & \mathbf x &\in \Omega \\ u(\mathbf x) &= g(\mathbf x) & \mathbf x &\in \Gamma_D \subset \partial\Omega \\ \nabla u(\mathbf x) \cdot \mathbf{\hat n}(\mathbf x) &= h(\mathbf x) & \mathbf x &\in \Gamma_N \subset\partial\Omega \end{align}\]

How is it satisfied?#

  • At grid points with rules to approximate derivatives

    • Finite Difference / collocation

  • A weak (integral) over elements with rules to reconstruct from element averages and define fluxes

    • Finite Volume

  • A weak form over elements with solution and “test functions” in the same space

    • Finite Element / Galerkin

How expensive is it?#

  • Suppose our domain is \(\Omega = (0, 1)^3\)

using Plots

# a common "good" discretiation
error(h) = min(1, 10*h^2)

function cost(h)
    n = 1/h # number of points per dimension
    N = n^3 # total number of grid points in 3D

function cost2(h)
    N = cost(h)
    N * log2(1 + N)
cost2 (generic function with 1 method)
plot([cost, cost2], error, .01, 1, xscale=:log10, yscale=:log10,
  xlabel="Cost", ylabel="Error")

What goes in? What comes out?#

  • Equations

    • Conservation

    • Variational principles

    • Materials

  • Geometry

    • meshing needed?

  • Boundary conditions

    • essential/Dirichlet

    • natural/Neumann

    • mixed

  • Initial conditions

  • Discrete solutions

    • Sampled on a grid

    • Time series

  • Quantities of Interest (QoI)

    • Heat flux

    • Maximum temperature

    • Maximum stress

    • Lift, drag

  • Eigensolutions

    • Resonant frequencies/modes

    • Orbitals

Who works with PDE solvers?#

  • Numerical analysts

  • Domain scientists and engineers

  • Materials scientists

  • HPC specialists

  • Optimizers

  • Statisticians

What skills should they have?#

Breakout groups: pick one role and make a list of essential and nice-to-have skills, plus at least one question. Pick one member to report out.

Computer Science = Runnable Abstraction Science#

In the “grinder”#

  • Mathematical language and principles for each major class of method

    • What is covered by theory

    • When you’re venturing off-trail

    • Analytic tools to predict and debug

  • Realistic cost and exploitable structure

    • Performance on modern hardware

    • Algebraic solvers and time integrators

  • Write/modify stand-alone code

  • Select and use (parallel) libraries

How can you trust the solution?#

  • Verification: solving the problem right

  • Validation: solving the right problem


  • Domain scientists, engineers

  • Optimizers, statisticians

  • Abstractions that reduce cognitive load

  • Metrics/visualization for decisions

On programming languages#


Reliable and popular for libraries (PETSc, etc.). “Simple”, unsafe, capable of encapsulation.


Popular with applications and some libraries. Powerful, but complicated and unsafe.


The OG of numerical computing. Good for “array programming”, but encapsulation is hard. Unsafe depending on dialect.


For ground-up examples in class and in activities. Capable of high performance, expressive multiple dispatch, works well in a notebook. Library ecosystem for PDEs is limited, but rapidly improving.


Good access to JIT, libraries like FEniCS. Poor native performance, but good libraries to compiled code.


New compiled language. Good performance, encapsulation, safety, static analysis. Poor libraries (so far).

You don’t need to know any of these well and you can choose the language for your project.

Intrinsic motivation#

I won’t grade and I won’t waste your time#

  • I will provide feedback

  • I will be a mentor and guide navigating this field

  • I will help you get what you want out of the class (with a dash of wholesome context)

  • My mom said, “basically, your professor is asking you to be an adult”. That was too flexible.

    In other classes, I didn’t feel like I could bounce back, but in this one I did.

    What about the letter grade?#

    • Collect a portfolio of the work and insights you’re most proud of

    • Individual meetings during the last week of the semester (mid-term preview)

    • We’ll have a conversation and you’ll propose a grade based on your portfolio

      • I’ll trust you.

    I can nudge upward when students are too modest – pretty common. In rare cases, I may adjust down.

    What I need from you: Growth Mindset#

    How will the semester look?#

    Lecture periods#

    • Refresh, introduce, activity/group discussion, reflect

    • Pointers to further resources/activities

    In-class and homework activities#

    • (Short) coding, experiments, presentation of results

    • Use the math and programming tools of the field

    • Open-ended, go further occasionally

    Learning plans#

    • Write, track, and revise a personal learning plan.

    • Meet/chat approx weekly to check in with partner

    Projects (second half of semester)#

    • Dig into community software for solving PDEs

    • Short presentation on how the community works

    • Key stakeholders

    • Strengths and weaknesses

    • Discuss and critique

    Community contribution#

    • Tutorial, documentation, performance study, comparison, new features, new application

    • Reflect in a short presentation


    Git and GitHub#

    • GitHub CLassroom to manage repositories

    • Write using notebooks and markdown

    • Review using GitHub tools and nbgrader

    • Discussions


    • RISE slides available on website

    • Activities with scaffolding

    • Nbgrader for writing feedback

    • Works with many languages; we’ll mostly use Julia

    • Collaborative mode (new)

    JupyterHub: coding.csel.io#

    • Nothing to install, persistent storage

    A note about illnesses#

    • It’s likely that there will be a new variant sometime this fall.

    • Bivalent vaccines are coming, but we don’t know how effective they will be against the next variant.

    • High-quality masks are effective.

    • You’re welcome to attend virtually (and please do if you have symptoms or likely infected).

    • I have a 2yo in childcare and 5yo in elementary school

    • They get colds often, and sometimes need to stay home. COVID in July sucked.

    • I’ll likely need to teach remotely at some times, or otherwise make it up to you

    Choices in scoping the class#


    Analysis first, confirm using numerics. Limited to simpler models.


    Numerics first, pointers to useful theory.

    Build from scratch#

    Limited to simpler models, but you’ll understand everything under the hood.

    Build using libraries#

    More installation and software layers, but can solve more interesting problems.