Experimental verification of the non-equilibrium model for predicting behavior in the char zone of a charring ablator Status report

Experimental simulation to establish accuracy of nonequilibrium flow model with system simulating charring during ablatio

Solution of the Frozen Flow Momentum Equation Status Report

Momentum equation solved for frozen flow in char zone of charring ablato

Comparison of Methods for Determining the Composition of Pyrolysis Products from the Degradation of Ablative Composites. Status report.

Determining composition of pyrolysis products from degradation of ablative material

A unifying view of optimism in episodic reinforcement learning

The principle of “optimism in the face of uncertainty” underpins many theoretically successful reinforcement learning algorithms. In this paper we provide a general framework for designing, analyzing and implementing such algorithms in the episodic reinforcement learning problem. This framework is built upon Lagrangian duality, and demonstrates that every model-optimistic algorithm that constructs anoptimistic MDP has an equivalent representation as a value-optimistic dynamic programming algorithm. Typically, it was thought that these two classes of algorithms were distinct, with model-optimistic algorithms benefiting from a cleaner probabilistic analysis while value-optimistic algorithms are easier to implement and thus more practical. With the framework developed in this paper, we show that it is possible to get the best of both worlds by providing a class of algorithms which have a computationally efficient dynamic-programming implementation and also a simple probabilistic analysis. Besides being able to capture many existing algorithms in the tabular setting, our framework can also address large-scale problems under realizable function approximation, where it enables a simple model-based analysis of some recently proposed methods

Tunneling and Non-Universality in Continuum Percolation Systems

The values obtained experimentally for the conductivity critical exponent in numerous percolation systems, in which the interparticle conduction is by tunnelling, were found to be in the range of $t_0$ and about $t_0+10$, where $t_0$ is the universal conductivity exponent. These latter values are however considerably smaller than those predicted by the available ``one dimensional"-like theory of tunneling-percolation. In this letter we show that this long-standing discrepancy can be resolved by considering the more realistic "three dimensional" model and the limited proximity to the percolation threshold in all the many available experimental studiesComment: 4 pages, 2 figure

Reversal-field memory in magnetic hysteresis

We report results demonstrating a singularity in the hysteresis of magnetic materials, the reversal-field memory effect. This effect creates a nonanalyticity in the magnetization curves at a particular point related to the history of the sample. The microscopic origin of the effect is associated with a local spin-reversal symmetry of the underlying Hamiltonian. We show that the presence or absence of reversal-field memory distinguishes two widely studied models of spin glasses (random magnets).Comment: 3 pages, 5 figures. Proceedings of "2002 MMM Conferece", Tampa, F

Hysteresis loops of Co-Pt perpendicular magnetic multilayers

We develop a phenomenological model to study magnetic hysteresis in two samples designed as possible perpendicular recording media. A stochastic cellular automata model captures cooperative behavior in the nucleation of magnetic domains. We show how this simple model turns broad hysteresis loops into loops with sharp drops like those observed in these samples, and explains their unusual features. We also present, and experimentally verify, predictions of this model, and suggest how insights from this model may apply more generally.Comment: 4.5 pages, 5 figure

Bandit problems with fidelity rewards

The fidelity bandits problem is a variant of the K-armed bandit problem in which the reward of each arm is augmented by a fidelity reward that provides the player with an additional payoff depending on how ‘loyal’ the player has been to that arm in the past. We propose two models for fidelity. In the loyalty-points model the amount of extra reward depends on the number of times the arm has previously been played. In the subscription model the additional reward depends on the current number of consecutive draws of the arm. We consider both stochastic and adversarial problems. Since single-arm strategies are not always optimal in stochastic problems, the notion of regret in the adversarial setting needs careful adjustment. We introduce three possible notions of regret and investigate which can be bounded sublinearly. We study in detail the special cases of increasing, decreasing and coupon (where the player gets an additional reward after every m plays of an arm) fidelity rewards. For the models which do not necessarily enjoy sublinear regret, we provide a worst case lower bound. For those models which exhibit sublinear regret, we provide algorithms and bound their regret

Reversal-Field Memory in the Hysteresis of Spin Glasses

We report a novel singularity in the hysteresis of spin glasses, the reversal-field memory effect, which creates a non-analyticity in the magnetization curves at a particular point related to the history of the sample. The origin of the effect is due to the existence of a macroscopic number of "symmetric clusters" of spins associated with a local spin-reversal symmetry of the Hamiltonian. We use First Order Reversal Curve (FORC) diagrams to characterize the effect and compare to experimental results on thin magnetic films. We contrast our results on spin glasses to random magnets and show that the FORC technique is an effective "magnetic fingerprinting" tool.Comment: 4 pages, 6 figure