A broad introduction to numerical algorithms used in scientific computing. The course begins with a review of the basic principles of numerical analysis, including sources of error, stability, and convergence. The theory and implementation of techniques for linear and nonlinear systems of equations and ordinary and partial differential equations are covered in detail. Examples of the application of these methods to problems in engineering and the sciences permeate the course material. Issues related to the implementation of efficient algorithms on modern high-performance computing systems are discussed.

This course, whose subject matter covers the entire universe, targets the frontiers of modern astrophysics. Topics include the planets of our solar system; the search for extrasolar planets and extraterrestrial life and intelligence; the birth, life, and death of stars; black holes; the zoo of galaxies and their evolution; the Big Bang and the expanding universe; and dark matter, dark energy, and the large-scale structure of the universe. This course is designed for the non-science major and has no prerequisites past high school algebra and geometry. High school physics would be useful, but is not required.

The solar system and planets around other stars; the structure and evolution of stars; supernovae, neutron stars, and black holes; gravitational waves; the interstellar matter; the formation and structure of galaxies; cosmology, dark matter, dark energy, and the history of the entire universe. Compared to AST 203, this course employs more mathematics and physics. Intended for quantitatively-oriented students.

Black holes are amazing: so much mass is contained in such a small region of space that nothing, not even light, can escape. In this class, we will learn to understand what black holes are, and (equally importantly) what they are not (sorry, science fiction!). We will grapple with the seeming simplicity of black holes and their weirdness. We will also study how black holes are discovered and how they give rise to some of the most astonishing phenomena in the Universe. We will cover concepts at the forefront of modern astronomy and physics and highlight the power of quantitative thinking (algebra only) and the scientific method.

The Space Physics Laboratory course provides undergraduates at all levels the opportunity to participate in a laboratory developing NASA space flight instrumentation. The courses teach space physics laboratory skills, including ultrahigh vacuum, space instrument cleanroom, mechanical, electrical, and other laboratory skills, which then allow students to propose and carry out a significant group research project in the Laboratory. The class sequence comprises two semesters with Astro 250 as a prerequisite for Astro 251, a credit bearing (P/F) course.

Power from the nucleus offers a low-carbon source of electricity. However, fission power presents risks: nuclear proliferation, accidents, attacks (c.f. Ukraine), and waste disposal. On the other hand, new designs promise improved safety and/or smaller unit sizes. Fusion carries less risk and R&D is making exciting progress. Furthermore, private companies are now promising to commercialize fusion in the 2030's. We will delve into the scientific underpinnings of these two energy sources, strengthening your physical insight and mathematical and computational skills, and giving you the tools to assess these issues for yourselves.

Stars form from interstellar gas, and eventually return material to the interstellar medium (ISM). Nuclear fusion powers stars, and is also the main energy source in the ISM. This course discusses the structure and evolution of the ISM and of stars. Topics include: physical properties and methods for studying ionized, atomic, and molecular gas in the ISM; dynamics of magnetized gas flows and turbulence; gravitational collapse and star formation; the structure of stellar interiors; production of energy by nucleosynthesis; stellar evolution and end states; the effects of stars on the interstellar environment.

Theoretical and numerical analysis of the structure of stars and their evolution. Topics include a survey of the physical process important for stellar interiors (equation of state, nuclear reactions, transport phenomena); and the integrated properties of stars and their evolution.

Selected astrophysical applications of electrodynamics, special and general relativity, nuclear and particle physics. Topics may include synchrotron radiation, comptonization, orbits and accretion in black-hole metrics, radio sources, cosmic rays, and neutrino astropysics.

Students improve their ability to give effective professional presentations, through lessons and opportunities to communicate their own research.

This is an introductory graduate course in plasma physics, focusing on magnetohydrodynamics (MHD) and its extension to weakly collisional or collisionless plasmas. Topics to be covered include: the equations of MHD and extended MHD, the structure of magnetic fields, static and rotating MHD equilibria and their stability, magnetic reconnection, MHD turbulence, and the dynamo effect. Applications are drawn from fusion, heliophysical, and astrophysical plasmas.

Introduction to theory of fluctuations and transport in plasma. Origins of irreversibility. Random walks, Brownian motion, and diffusion; Langevin and Fokker-Planck theory. Fluctuation-dissipation theorem; test-particle superposition principle. Statistical closure problem. Derivation of kinetic equations from BBGKY hierarchy and Klimontovich formalism; properties of plasma collision operators. Classical transport coefficients in magnetized plasmas; Onsager symmetry. Introduction to plasma turbulence, including quasilinear theory. Applications to current problems in plasma research.

Introduction to experimental plasma physics, with emphasis on high-temperature plasmas for fusion. Requirements for fusion plasmas: confinement, beta, power and particle exhaust. Discussion of tokamak fusion and alternative magnetic and inertial confinement systems. Status of experimental understanding: what we know and how we know it. Key plasma diagnostic techniques: magnetic measurements, Langmuir probes, microwave techniques, spectroscopic techniques, electron cyclotron emission, Thomson scattering.

Advances in experimental and theoretical studies or laboratory and naturally-occurring high-temperature plasmas, including stability and transport, nonlinear dynamics and turbulence, magnetic reconnection, selfheating of "burning" plasmas, and innovative concepts for advanced fusion systems. Advances in plasma applications, including laser-plasma interactions, nonneutral plasmas, high-intensity accelerators, plasma propulsion, plasma processing, and coherent electromagnetic wave generation.

Analysis of methods for the numerical solution of the partial differential equations of plasma physics, including those of elliptic, parabolic, hyperbolic, and eigenvalue type. Topics include finite difference, finite element, spectral, particle-in-cell, Monte Carlo, moving grid, and multiple-time-scale techniques, applied to the problems of plasma equilibrium, transport and stability. Basic parallel programming concepts are discussed.

Develop skills, knowledge, and understanding of basic and advanced laboratory techniques used to measure the properties and behavior of plasmas. Representative experiments are: cold-cathode plasma formation and architecture; ambipolar diffusion in afterglow plasmas; Langmuir probe measurements of electron temperature and plasma density; period doubling and transitions to chaos in glow discharges; optical spectroscopy for species identification; microwave interferometry and cavity resonances for plasma density determination; and momentum generated by a plasma thruster.

The course provides an introduction to modern statistics and data analysis. It addresses the question, "What should I do if these are my data and this is what I want to know"? The course adopts a model based, largely Bayesian, approach. It introduces the computational means and software packages to explore data and infer underlying parameters from them. An emphasis will be put on streamlining model specification and evaluation by leveraging probabilistic programming frameworks. The topics are exemplified by real-world applications drawn from across the sciences.