Theory & Simulation (TS) Projects

 

Waves and Waveguides:  From optics to oceans

Manager: Jared Rivera – In-House

Learning Objectives: Simulations, Programming

Applications open

Wave motion is pervasive in Physics and Astronomy, so being comfortable with the phenomena as well as its mathematical description is necessary for meaningful understanding of many physical processes. In this project we will investigate multiple types of waves in free space as well as in boundary-condition-enforced geometries (waveguides) through the analytic math, numerical solutions, and simulations. We’ll look at applications from the theory of wave optics to practical aspects of hydrology.


Efficient Electricity Generation via Novel Methods

Manager: Mercedeh Khazaieli – In-House

Learning Objectives: Programming (Python)

Applications: Closed

To meet increasing energy demands, scientists are researching electricity generation using novel methods such as thermoelectrics, photovoltaics, wave power, and other methods. Thermoelectric materials generate electricity from temperature differences between their layers, photovoltaics generate electricity using light, and wave power is a way to utilize wind waves to generate power. Team members will create simulations of these methods using Python, optimizing for maximized efficiency. These simulations can be used as a basis for actual power generators in the future.


Modeling Quantum Systems with Machine Learning

Manager: Joshua Wong – In-House

Learning Objectives: Programming (Python), Neural Networks

Applications: Open

Electrons are all over the place, and they’re complicated! If only we could somehow model their wavefunctions… but with so many in a given molecule, that’s practically impossible! I wonder, is there an easier way to model a complicated wavefunction…? Yes! We can use machine learning to do this!


Computational methods for physics

Manager: Suyash Kumar – In House

Learning Objectives:  Simulation, Machine Learning, Programming (Python, Octave)

Applications: OPEN

With the advent of computational power, the use of programming skills is becoming more central to both experimental and theoretical avenues of Physics. While labs deal very thoroughly with data analysis and plotting, two things often neglected are simulations and predictive models – and these can be great assets to a Physicist in order to extend and deepen their knowledge about physical systems. This project focuses on exactly these two aspects – simulation of various physical phenomena and development of predictive models via machine learning – and hopes to add to the already diverse skill set of Physics majors. 

Decoding Brain signals with deep learning

Manager:  William Zhu – In-house

Contact: williamzhu@g.ucla.edu

Learning Objectives:  Programming (Python, Keras, and Tensorflow), Neural Networks, Deep Learning

The inner workings of a brain is one of the greatest mysteries yet to be completely solved by science. Researchers need to make sense of an immense amount of brain signals in order to understand the mechanisms behind higher-order functions—such as perception, feelings, memory, or the sense of self—produced by this three-pound network of neurons. The neural networks used in deep learning, themselves inspired by the brain and designed to process large amounts of information, fit well with this task.

In the fall quarter, we will first gain experience with deep learning through practicing on simple image data sets with convolutional neural networks (CNNs). We will gain exposure to other types of neural net architecture as well, such as recurrent neural networks (RNNs) for time-domain signal analysis. Then, starting in the winter quarter, we will apply the skills and knowledge we have acquired to build a neural net that decodes behavioral and cognitive states from fMRI images (all data will be from public domain datasets, so we won’t work with actual fMRI machines. Sorry to disappoint you!). The exact research topic is subject to modification, depending on how everyone feels about coding or the ideas behind deep learning. At the very least, students will learn very important skills that are both handy in laboratories and sought-after in the job market, including Python, Keras, and TensorFlow.