Physics Special Topics Courses

Each PHYS 139 is a one-of-a-kind course created by our faculty for Physics majors.

Not all offerings require the universal prerequisites.

Check the course listing below for specific prereqs and enrollment info.

Some 139s that appear “full” may actually be closed to apply special prerequisites and/or to give Physics majors priority for seats. In these cases, students must submit an EASy request for preauthorization to enroll.

You can take up to two different PHYS 139 courses during your time at UCSD, as long as each covers a unique topic.

If you want to enroll in two PHYS 139s in the same term, enroll in the first through WebReg, then submit an EASy request to enroll in the second one, as the Registrar’s Office will need to enroll you.

PHYS 139/239 A00 - Understanding the Meissner Effect in Superconductors (same course as PHYS 139 from Fall 2024)

Jorge Hirsch

Click here for course website

The Meissner effect is the expulsion of a magnetic field from the interior of a metal when it becomes superconducting. It took 22 years to discover it experimentally after the discovery of superconductivity in 1911 because it was completely unexpected. It is generally believed to be well understood both macroscopically and microscopically through London’s equation (1933) and BCS theory (1957). In this course I will explain what is wrong with the general understanding of the Meissner effect,  and how to understand it correctly.  The correct understanding of the Meissner effect leads to a new understanding of what causes superconductivity in nature, qualitatively different from what BCS theory says. This leads to qualitatively different guidelines to guide the search for materials that will superconduct at room temperature.  

Prerequisites: PHYS 2A-B-C-D or 4A-B-C-D-E, MATH 20A-B-C or 31BH, and 18 or 20F or 31AH.

Undergraduate Students: This PHYS 139 can count toward the PHYS UD REs for PY26, PY29, PY30, PY31, PY32, and PY33, only. PHYS 139s are not guaranteed to be offered annually. 

Graduate Students: Please contact Sharmila to discuss options for having this apply to GROUP 2 of your degree requirements.

 

PHYS 139/239 B00 - Machine Learning in Physics (same course as PHYS 139/239 C00 from Winter 2023 and Spring 2024)

 

Javier Duarte

This course is an upper-division undergraduate course and introductory graduate course on machine learning in physics. No previous machine learning knowledge is necessary. However, some basic knowledge of calculus, linear algebra, statistics, and Python programming may be expected/useful. The course structure will consist of weekly lectures on conceptual topics, e.g. statistics, linear algebra, scientific data set exploration, feature engineering, (stochastic) gradient descent, neural networks, and unsupervised learning. Students will learn key concepts in data science and machine learning, including selecting and preprocessing data, designing machine learning models, evaluating model performance, and relating model inputs and outputs to the underlying physics concepts. We will apply these methods to the domains of collider physics, neutrino physics, astronomy, and potentially others. There will be 4 homework assignments. There will also be a final project in which students will work in groups to reproduce the results of an ML in physics research article. A midterm assignment to propose the project will also be required.  

Prerequisites: The standard prereqs will not apply. Instead, the prereqs will be MATH 18/31AH, 20C/31BH, and 20E/31CH. An EASy request will be needed to pursue enrollment, as this is the only way to enforce unique prereqs for Special Topics courses (the waitlist WILL NOT be processed). Physics majors will be given priority for available seats. 

Undergraduate Students: This PHYS 139 can count in place of PHYS 142 for PY33 or toward the PHYS UD REs for PY26, PY29, PY30, PY31, PY32, and PY33, only. PHYS 139s are not guaranteed to be offered annually. 

Graduate Students: Please contact Sharmila Poddar to discuss options for having this apply to GROUP 8 of your degree requirements.

 

PHYS 239 C00 - String Theory and Supersymmetry

Kenneth Intriligator

Strings and supersymmetry might exist in Nature.  The theories have also been fruitful theoretical frameworks for generating ideas and a deeper understanding about quantum field theory, gravity, and interconnections with mathematics.  This course will introduce and survey some of the key aspects, insights, and possible applications of the theories.  Some of the specific topics and applications will be chosen based on the background and interests of the enrolled students.  

Graduate Students: Please contact Sharmila Poddar to discuss options for having this apply to GROUP 3 of your degree requirements. 

 

PHYS 39 A00: Intro to Error Analysis & Uncertainty

Jessica Arlett

Physics 39 is a course that runs adjacent to the lab course Physics 2CL. This course equips you with skills in quantitative error analysis, without the lab component that 2CL has. In other words, you will not be doing the labs that 2CL students do, but you will be doing the homework and Final exam. You should attend the lectures, because a lot of the HW and Final exam content will be discussed in the lectures. A strong foundation in statistical principles is essential, as it enables you to distinguish between normal variations due to experimental uncertainty and significant differences in data. You will learn to apply statistical tools such as significant figures, uncertainty propagation, error analysis, normal distribution, t-scores, weighted averages, least squares fitting, and chi-squared tests. MATLAB will be introduced to help perform least squares fitting and enhance your data analysis. 

Prerequisites: Department approval based on course equivalency petition.

 

PHYS 139/239 A00 - Physics-Inspired Computing (click here for flyer; same course as PHYS 139 from Winter 2025)

 

Ivan Schuller and Massimiliano Di Ventra 

Physics-based, bio-inspired approaches to computation gained considerable attention in the past decades and are poised for further growth in the years to come. This is because the global energy required in computational tasks is growing exponentially vs. the world’s energy production, which grows only linearly. This class will discuss a wide variety of unconventional bio inspired computing approaches such as Neuromorphic Computing, Quantum Computing, and MemComputing. Unlike traditional Turing computation, typically using the von Neumann architecture, bio-inspired approaches use novel physical systems and phenomena to solve important computational problems such as combinatorial optimization, Machine Learning and quantum mechanical. This is the same special topic as “Neuromorphic Computing” taught in Winter 2023. Students who took the W23 offering are not eligible to enroll in the W25 offering. 

Prerequisites: PHYS 2A-B-C-D or 4A-B-C-D-E, MATH 20A-B-C or 31BH, and 18 or 20F or 31AH.

PHYS 39 A00: Intro to Error Analysis & Uncertainty

Alex Frano

Physics 39 is a course that runs adjacent to the lab course Physics 2CL. This course equips you with skills in quantitative error analysis, without the lab component that 2CL has. In other words, you will not be doing the labs that 2CL students do, but you will be doing the Homeworks and Final exams. You should attend the lectures, because a lot of the HW and Final exam content will be discussed in the lectures. A strong foundation in statistical principles is essential, as it enables you to distinguish between normal variations due to experimental uncertainty and significant differences in data. You will learn to apply statistical tools such as significant figures, uncertainty propagation, error analysis, normal distribution, t-scores, weighted averages, least squares fitting, and chi-squared tests. MATLAB will be introduced to help perform least squares fitting and enhance your data analysis. 

Prerequisites: Department approval based on course equivalency petition.

 

Archive of prior Special Topics Courses