I am a researcher, designer, and builder driven by the pursuit of fundamental truths—elegant, universal principles that reveal beauty within complexity. My perspective is shaped by a fusion of disciplines: a deep understanding of AI/ML, a well-honed spatial intuition, and the appreciation of mathematical rigor. These facets intertwine, constantly sparking a dynamic interplay of ideas.
PARTICLES LAB ｜ EXP-0
PARTICLES LAB is a personal initiative where I use WebGL/WebGPU and GPU programming (GPGPU, GLSL, custom compute shaders) to prototype and visualize elegant computational ideas encountered in — and emerging from — my AI/ML research. Particle systems offer a natural embodiment of the elements and principles I care about: discreteness, locality, physics, emergence, and optimization.
EXP-0 explores how structure can emerge from stochasticity—how simple, universal principles guide the collective dynamics of homogeneous particles into coherent, transient macrostructures. While grounded in physical simulation, the experiment echoes ideas in modern generative modeling and signal processing, including diffusion models and flow matching.
References
Ken Perlin. Improving noise. ACM Transactions on Graphics, 2002.
Jonathan Ho et al. Denoising Diffusion Probabilistic Models. Arxiv, 2020.
Tim Severien. Stable curl noise particles.
Isaac Cohen. GLSL implementation of curl noise.
Patricio Gonzalez Vivo. GLSL implementation of simplex noise.
The Book of Shaders. Chapter 11. Noise.
NVIDIA Developer. Geometry instancing on GPU.
© 2019-2025 Yuning Wu. Visuals, text, and creative content are licensed under CC BY-NC-ND 4.0 . No commercial use or derivative works permitted without explicit permission.