21,702 research outputs found
Potential Energy and Particle Interaction Approach for Learning in Adaptive Systems
Abstract. Adaptive systems research is mainly concentrated around optimizing cost functions suitable to problems. Recently, Principe et al. proposed a particle interaction model for information theoretical learning. In this paper, inspired by this idea, we propose a generalization to the particle interaction model for learning and system adaptation. In addition, for the special case of supervised multi-layer perceptron (MLP) training we propose the interaction force backpropagation algorithm, which is a generalization of the standard error backpropagation algorithm for MLPs
Determination of Interaction Potentials in Freeway Traffic from Steady-State Statistics
Many-particle simulations of vehicle interactions have been quite successful
in the qualitative reproduction of observed traffic patterns. However, the
assumed interactions could not be measured, as human interactions are hard to
quantify compared to interactions in physical and chemical systems. We show
that progress can be made by generalizing a method from equilibrium statistical
physics we learned from random matrix theory. It allows one to determine the
interaction potential via distributions of the netto distances s of vehicles.
Assuming power-law interactions, we find that driver behavior can be
approximated by a forwardly directed 1/s potential in congested traffic, while
interactions in free traffic are characterized by an exponent of approximately
4. This is relevant for traffic simulations and the assessment of telematic
systems.Comment: For related work see http://www.helbing.or
Deep learning as closure for irreversible processes: A data-driven generalized Langevin equation
The ultimate goal of physics is finding a unique equation capable of
describing the evolution of any observable quantity in a self-consistent way.
Within the field of statistical physics, such an equation is known as the
generalized Langevin equation (GLE). Nevertheless, the formal and exact GLE is
not particularly useful, since it depends on the complete history of the
observable at hand, and on hidden degrees of freedom typically inaccessible
from a theoretical point of view. In this work, we propose the use of deep
neural networks as a new avenue for learning the intricacies of the unknowns
mentioned above. By using machine learning to eliminate the unknowns from GLEs,
our methodology outperforms previous approaches (in terms of efficiency and
robustness) where general fitting functions were postulated. Finally, our work
is tested against several prototypical examples, from a colloidal systems and
particle chains immersed in a thermal bath, to climatology and financial
models. In all cases, our methodology exhibits an excellent agreement with the
actual dynamics of the observables under consideration
Neural Networks for Modeling and Control of Particle Accelerators
We describe some of the challenges of particle accelerator control, highlight
recent advances in neural network techniques, discuss some promising avenues
for incorporating neural networks into particle accelerator control systems,
and describe a neural network-based control system that is being developed for
resonance control of an RF electron gun at the Fermilab Accelerator Science and
Technology (FAST) facility, including initial experimental results from a
benchmark controller.Comment: 21 p
- …