A GNN-ODE surrogate forecasts reactor thermal-hydraulics under partial observability, achieving low MAE on held-out transients, fast inference, and recovery of a physical Reynolds-number exponent after fine-tuning on limited experimental data.
Stable and safe human-aligned reinforcement learning through neural ordinary differential equations
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A literature review of safe RL using Lyapunov and barrier functions that identifies a shift to model-free methods since 2017, well-defined open problems per approach class, and high-dimensional scalability as the main barrier.
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Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability
A GNN-ODE surrogate forecasts reactor thermal-hydraulics under partial observability, achieving low MAE on held-out transients, fast inference, and recovery of a physical Reynolds-number exponent after fine-tuning on limited experimental data.
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A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
A literature review of safe RL using Lyapunov and barrier functions that identifies a shift to model-free methods since 2017, well-defined open problems per approach class, and high-dimensional scalability as the main barrier.