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Found 5 papers, 0 papers with code
Variational Inference for SDEs Driven by Fractional Noise
In this paper, building upon the Markov approximation of fBM, we derive the evidence lower bound essential for efficient variational inference of posterior path measures, drawing from the well-established field of stochastic analysis.
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
We demonstrate learning of Lagrangian dynamics from images on the dm_control pendulum, cartpole and acrobot environments.
Physics Informed LSTM Network for Flexibility Identification in Evaporative Cooling Systems
These physics informed networks approximate the time-dependent relationship between control input and system response while enforcing the dynamics of the process in the neural network architecture.
Adaptive control of a mechatronic system using constrained residual reinforcement learning
We investigate how constraining the residual agent's actions enables to leverage the base controller's robustness to guarantee safe operation.
A Nonlinear Feynman-Kay Formula with Application in Linearly Solvable Optimal Control
In this article we present a solution to a nonlinear relative of the parabolic differential equation that was tackled by Feynman and Kac in the late 1940s.
Optimization and Control
