PRACTICAL LINEAR ALGEBRA / BROWSER TOOL
Add, subtract, and multiply matrices, then inspect eigenvalues and eigenvectors. Deeper numerical and smart-manufacturing experiments remain available as featured labs.
EVERYDAY LINEAR ALGEBRA
Start with routine matrix operations and an inspectable eigen-analysis workflow. When you need more depth, continue into SVD, QR, low-rank approximation, spectra, topology, and synthetic smart-fab experiments.
MATRIX WORKBENCH
Choose a size, enter matrices A and B, then calculate. Results remain local to your browser.
A and B must have the same dimensions.
MICRON-ALIGNED APPLICATION LAYER
Synthetic data only. Every result is deterministic, inspectable, and connected to an explicit assumption.
STATISTICS + NLA
Generate a synthetic wafer field, standardize each die, then group adjacent exceedances into interpretable spatial clusters.
Move across the wafer to inspect an individual die.
TOPOLOGY + GRAPH SEARCH
Treat distance as a neighborhood relation. Connected components reveal whether sensor evidence can propagate across the monitored tool area.
Move across a sensor to inspect its health and neighborhood.
ECONOMICS + SENSITIVITY
Translate demand, energy, exchange-rate pressure, and downtime into a normalized operational scenario. It is a decision sandbox, not a forecast.
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C ALGORITHMS + OPERATIONS
Stable sorting turns hot-lot status, quality risk, waiting time, and due-date slack into a visible priority order with no hidden model.
Scores are transparent heuristics for demonstration, not production scheduling rules.
INTERACTIVE EXPERIMENTS
| Method | Orthogonality | Reconstruction |
|---|
EVIDENCE MAP
SVD, QR, low-rank approximation, spectra, and Schur complements support dimensionality reduction and model validation.
Open-neighborhood and connectedness thinking becomes a sensor graph with coverage and isolation diagnostics.
Demand, cost, exchange-rate, and shock sensitivity become an explicit operational stress-test.
C sorting/search habits and Python data workflows become a stable, explainable dispatch queue.
DESIGN PRINCIPLES
No confidential fab data and no black-box numerical package are required. Core routines live in tested modules; assumptions, limitations, and outputs remain visible.
Inputs, derived metrics, and visual outputs stay connected.
Seeded simulations make experiments repeatable.
Factorizations are checked by orthogonality and residual tests.
Static deployment works on GitHub Pages without a backend.