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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.
Generalizing Impermanent Loss on Decentralized Exchanges with Constant Function Market Makers
To this end, we provide a framework to generalize impermanent loss for multiple asset pools obeying any constant function market maker with optional concentrated liquidity.
Physics-constrained Bayesian inference of state functions in classical density-functional theory
In our case, the output of the learning algorithm is a probability distribution over a family of free energy functionals, consistent with the observed particle data.
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