… using Bayesian inference to learn Hamiltonian parameters, we will … seldom have consistent
Hamiltonians from experiment to … Hamiltonian learning problem from one involving learning …

… learn the unknown Hamiltonian evolution with efficient running time scaling and robustness
… To address the exponential scaling complexity, we assume that the Hamiltonian is sparse …

… realizations, we learn nonlinear disturbance features using a Hamiltonian-based neu…
Hamiltonian dynamics. We develop a geometric adaptation law to estimate the disturbances online …

… function, the online actor is added to learn the optimal control policy simultaneously. In the
… , the traditional Hamiltonian is extended as quasiHamiltonian and pseudo-Hamiltonian for …

… We present a method for learning generalized Hamiltonian decompositions of ordinary …
to adjoint methods while being significantly more robust to noise than derivative regression. …

… Section V provides theoretical evidence that the learning algorithm is robust to depolarizing
noise and that … (Color online) Quadratic loss as a function of number of experiments for …

… We prove that the BHL estimator is robust to a large class of measurement errors, and we …
to implement an online version of BHL. This enables the estimation of Hamiltonians in real-…

… robustness and provide a rationale for this observation. … We propose Hamiltonian-inspired
graph neural ODEs, noted for … that Hamiltonian flow GNNs present enhanced robustness …

… This section evaluates our Hamiltonian dynamics learning and safe tracking control … to the
end of the reference path while avoiding sensed obstacles online, ie, d(p, O) remains positive …

… guarantees about their stability and robustness. As a result, many machine learning techniques
are still limited to being used in situations where safety and robustness are not critical for …