Mechanism learning infers active local evolution rules via prototype-anchored descriptors to achieve more robust forecasting than direct state prediction on benchmarks like Burgers, WeatherBench2, and Lorenz96.
Linear predictors for nonlin- ear dynamical systems: Koopman operator meets model predictive control.Automatica, 93:149–160, 2018
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CCSS-IX is a context-conditioned structured simulator for wastewater digital twins that uses adaptive expert mixing and self-falsifying conformal decision rules to reduce unsafe actions while maintaining low prediction error on real plant and benchmark data.
WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.
Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.
citing papers explorer
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Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting
Mechanism learning infers active local evolution rules via prototype-anchored descriptors to achieve more robust forecasting than direct state prediction on benchmarks like Burgers, WeatherBench2, and Lorenz96.
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Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support
CCSS-IX is a context-conditioned structured simulator for wastewater digital twins that uses adaptive expert mixing and self-falsifying conformal decision rules to reduce unsafe actions while maintaining low prediction error on real plant and benchmark data.
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WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos
WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.
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Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
Koopman models identified via meta-heuristic EDMD from engine simulations enable an adaptive MPC with disturbance observer and a feedback linearization controller that achieve comparable steady-state performance with the adaptive version showing superior robustness under varying conditions.