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-title: Power Grid Transfer Learning CSD
+title: Eigenvalue early warning for power grid stability — the physical basis AI transfer-learning models learn to detect
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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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+TaiScience CSD-EWS Simulator
+TaiScience CSD-EWS is an educational simulator for critical slowing down (CSD) as an early-warning signal in power grid stability. It visualizes how system eigenvalues (poles) approach the stability boundary, using a Jacobian eigenvalue proxy and damping-ratio thresholds.
+This work is part of the TokaMind transfer-learning framework, which adapts a tokamak-plasma foundation model to power-grid dynamics. While Microsoft's GridSFM addresses static AC-OPF feasibility, this system targets dynamic CSD precursors — complementary directions for grid security analysis, relevant to cascading-failure events such as the 2025 Iberian blackout and the 2016 South Australia blackout.
+What It Shows
+Complex s-plane map — conjugate pole pair (s = σ ± jω) with real-time position relative to the stability boundary (Re = 0)
+Damping ratio — ζ = −σ / √(σ² + ω²), with a 5% threshold separating adequate damping from warning and danger zones
+Time-domain response — illustrative y(t) = e^(σt)·cos(ωt) showing how a near-critical mode decays or diverges
+Core Concept
+Critical slowing down is a generic precursor to instability across dynamical systems: as a control parameter pushes a system toward a bifurcation, its dominant eigenvalue's real part approaches zero, recovery from perturbations slows, and damping collapses. In power systems, detecting this slowing before a mode crosses into the right-half plane provides an early-warning window for operators.
+The full CSD detection pipeline (time-domain Jacobian proxy + spectral LFPR engine) is released as open source:
+https://github.com/chihsingwu/CSD-dual-engine
+Use Case
+Teaching and intuition-building for power-system stability monitoring, transient stability assessment (TSA), and PMU-based early-warning research. This page is a manual teaching demo — not a live PMU or Toeplitz identification system. The waveform shows e^(σt)·cos(ωt) only, without modal amplitude or phase.
+Related Work
+TaiScience Research Group (Fu Jen Catholic University): https://taiscience.org
+StromaPath platform: https://stromapath.com
+TokaMind PMU transfer-learning preprint: https://arxiv.org/abs/2605.11033
+CSD dual-engine (open source): https://github.com/chihsingwu/CSD-dual-engine
+Keywords
+`critical slowing down` · `power grid early warning system` · `Jacobian eigenvalue proxy` · `damping ratio monitoring` · `transient stability assessment` · `PMU` · `TokaMind transfer learning` · `s-plane pole map`