The Concept

Why Mathematics and Emotion

Every piece in this collection begins with a question: what does this emotion look like as an equation?

Grief decays exponentially — intense at first, diminishing over time, but never reaching zero. Growth branches fractally — each new level of complexity generated by the same recursive rule. Connection follows coupled oscillators — two systems influencing each other's rhythm without ever fully merging.

These are not metaphors. The mathematical functions genuinely behave the way the emotions do. The equation at the bottom of each piece is not decoration. It is the reason the image looks the way it does.

How Each Series Is Made

Research. Before a single line is rendered, the emotion is studied across millennia of human art, philosophy, and science. What does this feeling do over time? Does it decay? Oscillate? Branch? Converge?

Mathematical selection. The function class is chosen because its behavior mirrors the emotion's dynamics. Exponential decay for grief. L-system branching for growth. Parametric coupling for connection.

Iteration. Hundreds of parameter variations are rendered in Python. The palette, the density, the negative space — every visual property maps to a mathematical parameter.

Human curation. Most renders are rejected. The ones that survive pass a simple test: does this image make you feel the emotion it claims to represent?

The Code

# GRIEF — Settling
# f(t) = e^(-λt) · cos(ωt)
t = np.linspace(0, 1, 2000)
lam = 3.5   # decay rate — how fast grief fades
omega = 22  # frequency — how often it returns
ys = baseline + PH*0.44 * np.exp(-lam*t) * np.cos(omega*np.pi*t)

The decay rate (λ) controls how fast the waves shrink. The frequency (ω) controls how often grief returns. The parameters were chosen because they felt true.

This Is Not AI-Generated Art

AI image generators — Midjourney, DALL-E, Stable Diffusion — take a text prompt and pattern-match against billions of training images. They produce visually impressive outputs with no underlying mathematical structure, no intentional emotional architecture, and no verifiable truth.

We went the opposite direction. We started with the emotion. We found the mathematics. We wrote the code. We rendered, evaluated, and curated. The process took months. Each series represents dozens of hours of research, coding, and aesthetic judgment.

The equation at the bottom of every piece is not a label. It is the proof.

“Built by a human. Rendered in Python. Grounded in mathematics. Every piece is the result of asking one question: what does this emotion look like as an equation?”

By the Numbers

200+

renders made across all series

130

pieces that survived curation

human emotions explored

100s

of hours of mathematical research

AI image generators used

300 DPI

print resolution on every file

Art Historical Context

This work stands in a tradition of artists who used reduction and restraint as a means of intensification:

Hasegawa Tōhaku's Pine Trees — the negative space is as carefully composed as the brushwork. What is not painted matters as much as what is.

Mark Rothko's color fields — emotion rendered through large-scale color relationships, with no representational content. The feeling is in the field itself.

Agnes Martin's grids — mathematical structure used as a vehicle for meditative calm. The geometry is the experience.

Mathematical Affect adds one dimension to this tradition: every visual decision can be traced back to a mathematical function. The geometry is not arbitrary. The equation is the reason the image looks the way it does.

The Standard

The final question for every piece: if this were hanging in a gallery with no label, would a viewer stop and feel something?

The equation at the bottom of each piece is real. It is the reason the image looks the way it does. This is not a style filter applied to a photograph. This is what the function actually looks like when you render it.