Courses from 1000+ universities
17 years ago, Krishna Kumar started offering free PMP prep online. Today, it’s a leading digital upskilling platform that helps millions upskill in AI, cybersecurity, data science, and more.
600 Free Google Certifications
Personal Creativity
Entrepreneurship
Instructional Design
Ecology and Wildlife Conservation
The Science of Well-Being
Mountains 101
Organize and share your learning with Class Central Lists.
View our Lists Showcase
Learn to solve high-order linear differential equations using characteristic polynomials and linear superposition. Covers guessing solutions, finding general solutions, and applying initial conditions.
Comprehensive derivation of the heat equation, exploring its mathematical foundations and physical principles in thermal energy conservation and transfer.
Explore Stokes' and Green's theorems, their geometric interpretations, and practical applications in mathematical physics, including examples and land area calculations.
Explore the divergence operator in vector calculus, understanding its role in quantifying local expansion or contraction of vector fields, with examples and applications in fluid dynamics.
Comprehensive exploration of deep reinforcement learning techniques, including Q-learning, actor-critic methods, policy networks, and gradient optimization for advanced AI decision-making and control systems.
Explore Q-learning and temporal difference algorithms in reinforcement learning, including SARSA and connections to biological learning through dopamine. Covers key concepts for deep reinforcement learning.
Explore dynamic programming in model-based reinforcement learning, covering policy iteration, value iteration, and Q-learning for effective decision-making strategies.
Explore machine learning's potential to enhance computational fluid dynamics, covering applications in simulations, turbulence modeling, and reduced-order models while emphasizing physics-informed approaches.
Explore deep learning techniques for discovering effective coordinate systems in dynamical systems, enabling simpler models and physical law discovery. Focus on autoencoders and physics-informed machine learning.
Explore a novel method combining deep learning and symbolic regression to uncover physical laws from data, with applications in physics and cosmology.
Explore how deep learning revolutionizes turbulence modeling in fluid dynamics, focusing on RANS equations and large eddy simulations for advanced computational techniques.
Explore turbulence closure models, focusing on RANS and LES approaches for approximating complex fluid dynamics. Learn key concepts and applications in scientific computing.
Explore turbulence in fluid dynamics through canonical examples and real-world applications. Discover engineering implications and gain insights into this fascinating phenomenon.
Explore the fundamental characteristics of turbulent fluid dynamics, their prevalence in nature and engineering, and the role of Reynolds number in controlling complexity.
Explore neural networks in deep reinforcement learning for control systems, from game-playing AI to robotics. Learn about key algorithms and groundbreaking applications.
Get personalized course recommendations, track subjects and courses with reminders, and more.