Physics through Computational Thinking Course | IISER Bhopal
Course Details
| Exam Registration | 33 |
|---|---|
| Course Status | Ongoing |
| Course Type | Core |
| Language | English |
| Duration | 8 weeks |
| Categories | Physics |
| Credit Points | 2 |
| Level | Undergraduate |
| Start Date | 19 Jan 2026 |
| End Date | 13 Mar 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 16 Feb 2026 |
| Exam Date | 28 Mar 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Bridging Physics and Computation: A New Way to Learn
In today's data-driven world, the ability to translate complex physical phenomena into computational models is an invaluable skill. The course Physics through Computational Thinking, offered by Prof. Auditya Sharma of IISER Bhopal, is designed to equip undergraduate students with precisely this capability. This 8-week journey moves beyond traditional problem-solving, emphasizing a cognitive shift towards computational approaches to understand fundamental physics.
The core philosophy is powerful yet straightforward: start with a problem you can solve by hand, translate it into a language a computer understands, verify the computational results against the known analytical solution, and then use that validated approach to tackle more complex, real-world problems that defy simple pen-and-paper analysis.
Meet Your Instructor: Prof. Auditya Sharma
Prof. Sharma brings a wealth of international research experience to the classroom. His academic journey includes:
- B.Tech in Engineering Physics from IIT Madras (2006).
- Ph.D. in Physics (Statistical Physics) from UC Santa Cruz (2011).
- Postdoctoral research at the International Institute of Physics, Natal, Brazil (2011-2013).
- Postdoctoral research at Tel Aviv University, Israel (2014-2015).
- Faculty member in the Department of Physics at IISER Bhopal since 2015.
This diverse background ensures the course is grounded in rigorous physics while embracing modern computational techniques.
Course Structure: An 8-Week Roadmap
The course is meticulously structured to build your skills from the ground up. Here’s a week-by-week breakdown:
| Week | Topic | Key Focus |
|---|---|---|
| 1 | Intro to Computational Thinking, Visual Thinking and Mathematica | Foundational mindset and tool introduction. |
| 2 | Dimensional Analysis & Non-dimensionalization | Identifying the core scales of a physical problem. |
| 3 | Data Analysis: Errors and Curve Fitting | Handling real data and extracting meaningful parameters. |
| 4 | Periodic Motion 1: Simple, Damped, Anharmonic Oscillators | Applying computational thinking to classical dynamics. |
| 5 | Dynamics through Numerical Methods (Euler, RK methods) | Core algorithms for solving equations of motion. |
| 6 | Periodic Motion 2: Forced Oscillations, Resonance, Friction | Exploring more complex dynamical systems. |
| 7 | Introduction to Monte Carlo Simulation | Probabilistic methods for complex systems. |
| 8 | Introduction to Random Walks | Foundational model for diffusion and stochastic processes. |
Who Should Take This Course?
This course is ideal for undergraduate students in Physics, Engineering Physics, or related fields who have completed foundational courses in:
- Newtonian Mechanics
- Modern Physics
- Electrostatics
The focus is not on advanced physics content but on developing a new problem-solving skill set applicable across scientific domains.
Industry Relevance and Applications
The skills taught in this course are highly sought after in industries that rely on quantitative modeling and simulation. This includes:
- Quantitative Finance: For modeling market dynamics and risk.
- Scientific Consulting & R&D: For simulating physical processes and analyzing complex data.
- Data Science: The core tenets of modeling, simulation, and error analysis are directly transferable.
Learning Resources and Approach
The course lectures are designed to be self-contained. For additional reference, standard undergraduate textbooks can be helpful aids:
- Mechanics: Kleppner and Kolenkow
- Electrostatics: Griffiths
- Modern Physics: Beiser
The pedagogical approach is hands-on and iterative. You will learn by doing—transforming abstract physics concepts into code, visualizing the results, and building confidence by cross-verifying computational and analytical solutions. This method ensures a deep, practical understanding that is far more durable than rote learning.
Why Computational Thinking in Physics?
Computational thinking in physics is more than just coding. It's a framework for breaking down problems, abstracting key variables, designing algorithmic steps for a solution, and analyzing the results. This course empowers you to:
- Solve problems that are analytically intractable.
- Gain intuitive insight through visualization.
- Develop a versatile skill set crucial for modern scientific research and industry.
By the end of these eight weeks, you will not only view physics problems differently but also possess a powerful toolkit to explore the natural world through the lens of computation.
Enroll Now →