Cross-Domain Long-Term Forecasting: Radiation Dose from Sparse Neutron Sensor via Spatio-Temporal Operator Network
This addresses the challenge of long-term forecasting in scientific machine learning for applications like physics, climate, and energy systems, though it is incremental in extending operator learning to cross-domain settings.
The paper tackled the problem of forecasting unobservable physical quantities from sparse, cross-domain sensor data by introducing the Spatio-Temporal Operator Network (STONe), which achieved accurate 180-day forecasts with millisecond inference latency using 23 years of global neutron data.
Forecasting unobservable physical quantities from sparse, cross-domain sensor data is a central unsolved problem in scientific machine learning. Existing neural operators and large-scale forecasters rely on dense, co-located input-output fields and short temporal contexts, assumptions that fail in real-world systems where sensing and prediction occur on distinct physical manifolds and over long timescales. We introduce the Spatio-Temporal Operator Network (STONe), a non-autoregressive neural operator that learns a stable functional mapping between heterogeneous domains. By directly inferring high-altitude radiation dose fields from sparse ground-based neutron measurements, STONe demonstrates that operator learning can generalize beyond shared-domain settings. It defines a nonlinear operator between sensor and target manifolds that remains stable over long forecasting horizons without iterative recurrence. This challenges the conventional view that operator learning requires domain alignment or autoregressive propagation. Trained on 23 years of global neutron data, STONe achieves accurate 180-day forecasts with millisecond inference latency. The framework establishes a general principle for cross-domain operator inference, enabling real-time prediction of complex spatiotemporal fields in physics, climate, and energy systems.