Gaia bridges atmospheric physics and advanced deep learning to forecast fold bifurcations and critical transitions in global climate elements up to 28 months in advance with zero look-ahead bias.
Climate tipping points represent critical thresholds where Earth systems undergo self-reinforcing, abrupt transitions. Traditional Earth System Models (CMIP6) struggle with computational latency and parameterization limits when forecasting these rare non-linear bifurcations.
As a dynamical system approaches a bifurcation threshold, its recovery rate from perturbations approaches zero—evidenced by rising Lag-1 autocorrelation and rolling variance.
We embed Navier-Stokes fluid dynamics and thermodynamic energy conservation directly into neural network loss residuals, ensuring models respect physical laws under extreme climate forcing.
By detecting pre-bifurcation anomalies up to 28 months prior to structural collapse, Gaia provides policymakers and scientific institutions critical lead time to enact mitigation strategies.
From Temporal Fusion Transformers with Variable Selection Networks to Graph Convolutional Networks modeling global atmospheric teleconnections.
State-of-the-art multi-horizon attention architecture incorporating Variable Selection Networks (VSNs), Gated Residual Networks (GRNs), and static metadata enrichment.
Efficient long-sequence time-series forecasting transformer utilizing ProbSparse self-attention and generative decoder distilling to achieve O(L log L) time and space complexity.
Standard encoder-only transformer adapted for continuous geospatial time series with learnable sinusoidal positional encodings and a dedicated classification token head.
Continuous real-time anomaly tracking across ocean circulation, polar ice sheets, and tropical ecosystems.
The system of ocean currents carrying warm water from the tropics into the North Atlantic. Critical slowing down is evidenced by increased variance and rising Lag-1 autocorrelation in RAPID-MOCHA transport time series.
Non-linear transition of rainforest savanna ecosystems due to deforestation, moisture recycling failure, and lengthened dry seasons.
Engineered to meet the rigorous publication criteria of NeurIPS, ICML, ICLR, CVPR EarthVision, NASA, and ISRO.
All time-series normalizers and feature extraction algorithms execute causally on historical training windows only, eliminating data leakage.
Embedding Navier-Stokes and energy balance PDEs directly into neural network loss functions ensures conservation of mass and heat.
Temporal Fusion Transformers predict fold bifurcation transitions up to 28 months in advance with quantile confidence intervals.