Computational Physics Courses and Certifications

Inside Livermore Lab

Neural Differentiable Physics: Unifying Numerical PDEs and Deep Learning for Data-Augmented Computational Physics

Explore Neural Differentiable Physics, unifying numerical PDEs and deep learning for enhanced computational modeling of complex physical systems. Learn innovative SciML approaches integrating physics principles with AI.

• YouTube
• 1 hour 28 minutes
• On-Demand
• Free Video

IPhT-TV

Computational Quantum Many-Body Physics: A Few Highlights

Explore computational approaches in quantum many-body physics, focusing on key highlights and advancements in the field.

• YouTube
• 36 minutes
• On-Demand
• Free Video

MonashPhysicsAndAstronomy

Queering Computation and Modeling in an Agency-Based Computational Physics Course

Explore how inclusive computational physics education can be reimagined through Making, creative expression, and agency-centered learning approaches that challenge traditional teaching methods.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

计算物理

随着计算机软硬件的快速发展，计算物理已经发展成物理学研究以及相关工程技术领域中一个重要的手段。本课程将简要介绍计算物理所涉及的诸多知识点，并展开相关实践。

• XuetangX
• On-Demand
• Free Online Course

IISER - Bhopal;  NPTEL

Physics through Computational Thinking

Hands-on introduction to computational thinking in physics, emphasizing problem translation, visual aids, and programming tools to develop analytical and computational problem-solving skills.

• Swayam
• 8 weeks
• On-Demand
• Free Online Course

Classical Physics: Journey Through the Laws of the Universe

352 ratings at Udemy

Peer beneath the surface of reality, understand how the universe works, and develop physics apps that make a difference!

• Udemy
• 20 hours 18 minutes
• On-Demand
• Paid Course

Paul G. Allen School

Computational Complexity and Explanations in Physics

Explore how computational intractability shapes physical explanations, from quantum mechanics interpretations to black hole paradoxes and thermodynamics.

• YouTube
• 1 hour 11 minutes
• On-Demand
• Free Video

Alan Turing Institute

Leveraging Physics-Induced Bias in Scientific Machine Learning for Computational Mechanics

Explore physics-induced bias in scientific machine learning for computational mechanics, focusing on physics-informed, structure-preserved learning for irregular geometries.

• YouTube
• 1 hour 8 minutes
• On-Demand
• Free Video

BIMSA

Counting Problems, Partition Functions, Statistical Physics, and Computational Complexity

Explore the intricate connections between counting problems, partition functions, statistical physics, and computational complexity in this mathematical analysis.

• YouTube
• 1 hour
• On-Demand
• Free Video

NPTEL;  IIT Bombay

Many body methods in quantum chemistry

Explore fundamental many-body methods in quantum chemistry, deriving equations for atomic and molecular structure, properties, and spectra. Gain insights for computational physics and chemistry applications.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

Numberphile

New Recipe for Pi - A Novel Approach to Calculating Pi

Discover a groundbreaking approach to calculating pi, featuring insights from physicists Sinha and Saha on their innovative method that connects string theory to this fundamental constant.

• YouTube
• 14 minutes
• On-Demand
• Free Video

Sabine Hossenfelder

Wolfram's Hypergraphs - A New Approach to Unified Physics Theory

Explore how Stephen Wolfram's revolutionary approach using hypergraphs and computer code could potentially unify fundamental physics laws and resolve conflicts with Einstein's theories.

• YouTube
• 12 minutes
• On-Demand
• Free Video

3Blue1Brown

The Physics Behind Colliding Blocks That Compute Pi

Delve into the physics behind how colliding blocks compute pi, exploring the mathematical principles, geometric puzzles, and small angle approximations that explain this fascinating phenomenon.

• YouTube
• 28 minutes
• On-Demand
• Free Video

Steve Brunton

Physics Informed Neural Networks (PINNs) - Introduction and Applications

1 review

Introduction to Physics Informed Neural Networks (PINNs): combining neural networks with physical laws to solve complex problems in fluid dynamics and beyond. Explores advantages, applications, and extensions of this innovative approach.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Steve Brunton

Coding a Fourth-Order Runge-Kutta Integrator in Python and Matlab

Learn to implement a 4th-order Runge-Kutta integrator in Python and Matlab, and apply it to simulate the chaotic Lorenz 1963 system. Gain practical coding skills for numerical integration.

• YouTube
• 37 minutes
• On-Demand
• Free Video