Simulating acoustic wave diffraction through a single slit using k-Wave and COMSOL. This post covers the physics of wave diffraction, problem setup, and comparison between pseudospectral and finite element methods.
Simulating acoustic wave propagation through a multi-layer medium with varying sound speed and density. This post covers the underlying physics, problem setup, and numerical implementation using k-Wave and COMSOL.
Learn how the k-space pseudospectral method achieves spectral accuracy with the flexibility of finite differences. The secret weapon behind k-Wave and modern acoustics simulation.
Dirichlet, Neumann, or Robin? Understanding boundary conditions is essential for correctly setting up PDE problems. Learn the physical meaning behind each type and when to use them.
A comprehensive comparison of Finite Difference, Finite Element, and Spectral Methods for solving PDEs. Learn the theory through the 1D wave equation, implement in Python, and understand when to use each approach.
Elliptic, Parabolic, or Hyperbolic? Understanding the three fundamental types of PDEs is the first step to mastering physics simulations. It's not just math—it's the language of waves, diffusion, and equilibrium.
This comprehensive guide walks you through the process of setting up R in Visual Studio Code, including installing necessary extensions, configuring the environment, executing R code interactively, and utilizing advanced debugging techniques. Enhance your R programming experience with integrated tools and optimizations for efficient development.
An in-depth comparison of tensors as used in Artificial Intelligence/Deep Learning versus Physics/Mechanics. While sharing the same name, these concepts have distinct meanings, properties, and applications. This post bridges the gap between these two worlds.
Why do humanoid robots and realistic CGI sometimes give us the creeps? Exploring the psychology behind the Uncanny Valley effect, from Masahiro Mori's theory to horror film techniques.
This post explores the distinctions between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). It delves into their definitions, hierarchical relationships, key comparisons, algorithms, models, and application scenarios. By understanding these differences, readers can better appreciate how AI serves as the overarching goal, ML as the approach, and DL as the tool for tackling complex tasks across various domains.