Computation in Physics
Physics has traditionally been divided into two broad categories: experimental and theoretical physics. Recently this categorization has become inadequate as all fields of physics have taken advantage of innovations in computing power. Experimental physicists can now analyze extraordinary amounts of data and also search for patterns in immense and complex data sets. Theoretical physicists use computing power to solve equations and simulate complex systems with great accuracy. These new applications of computers into physics can be thought of as a new category in their own right - computational physics. More than any time before, it is imperative that students who want to pursue a career in physics (or any science) learn data and computing techniques to tackle problems in contemporary science.
Questions addressed by computers in physics
- What are the fundamental building blocks of the universe?
- Machine learning is used to identify relevant or important data in particles physics collider experiments. For example, finding what a Higgs boson decays to at the Large Hadron Collider (LHC)
- How can we learn more about black holes?
- Using computers to accurately pick out interesting features in astrophysical data such as supernovae and black holes
- Can we find the best material for a given application?
- Materials physics relies on accurate calculations of material properties (e.g. strength, conductivity etc.) using the computational method Density Functional Theory
- How do random events give rise to observed phenomena?
- High-performance computers serve as virtual ‘laboratories’ by simulating stochastic or random events.
Computation in Physics at UE
The curriculum in the Department of Physics at UE is designed to prepare students for a satisfying and rewarding career after graduation. Apart from a well-rounded education in the major areas of physics, the department also stresses the importance of computational methods in the field:
- while taking the first-year sequence of physics, students have the option to enroll in PHYS 220 and 221 where numerical methods of calculation and simulations are introduced through the language of VPython.
- PHYS 340 - Computational Physics - offers students the opportunity to learn and utilize numerical methods to solve advanced physics problems involving for instance, differential equations, eigenvalues problems, and Monte Carlo techniques.
Opportunities for computation-based research and projects with faculty exist. Recent examples are:
- UE physics student Richard Gerst simulated charge and spin diffusion in organic semiconductors which will provide insight into organic solar cell operation. Richard worked on implementing code parallelization to speed up calculations and devised new more efficient ways to analyze results
- UE physics/data science student used VPython to model electron dynamics in stellar conditions
- UE students worked in conjunction with NASA's Goddard Space Flight Center to analyze and model rocketry data to extract information on ionospheric plasmas
The importance of a program like CiSM in UE physics is reflected by the paths of many graduates' careers whether they attend graduate school or go on into industry
- Physics/math double major Kelly Mosesso, after graduation from UE, entered Harvard's biostatistic program
- Physics /biology major Adam Dillman first entered industry at a biotech company involved in DNA sequencing. The data intensive work took DNA segments and processed and aligned them to form a complete sequence which could be used in forensic applications
“University courses tends to focus on the theoretical (for good reason), however I feel that something like CiSM could supplement coursework with practical skills that will help students hit the ground running when they begin their careers. Something like CiSM certainly would have helped me advance my career more quickly.”Adam Dillman, Physics and Biology 2011