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cs.LG2026
Verifier-Guided Model Discovery for Physical Dynamical Systems with Pretrained Symbolic Transformers
Farbod Faraji, Francesco Belardinelli
Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making. Yet high-fidelity simulations are prohibitively costly, and machi…
cs.LG2026
Safe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
Alexander W. Goodall, Francesco Belardinelli
Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications. In this paper, we…