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Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation
To overcome this inherent limitation, we adopt a NODE approach to learn, and at the same time predict, the difference between the actual electricity-price time series and the simulated price trajectories generated by the LE.
Physics-informed Bayesian inference of external potentials in classical density-functional theory
We combine a Bayesian inference approach with the classical DFT apparatus to reconstruct the external potential, yielding a probabilistic description of the external potential functional form with inherent uncertainty quantification.
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