Recognition of adsorption phase transition of polymer on surface by neural network

In a recent paper (Gislén et al. 1989) a convenient encoding and an efficient mean field algorithm for solving scheduling problems using a Potts neural network was developed and numerically explored on simplified and synthetic problems. In this work the approach is extended to realistic applications both with respect to problem complexity and size. This extension requires among other things the interaction of Potts neurons with different number of components. We analyze the corresponding linearized mean field equations with respect to estimating the phase transition temperature. Also a brief comparison with the linear programming approach is given. Testbeds consisting of generated problems within the Swedish high school system are solved efficiently with high quality solutions as results.

Download Full-text

The critical surface fugacity for self-avoiding walks on a rotated honeycomb lattice

Discrete Mathematics & Theoretical Computer Science ◽

10.46298/dmtcs.2330 ◽

2013 ◽

Vol DMTCS Proceedings vol. AS,... (Proceedings) ◽

Author(s):

Nicholas R. Beaton

Keyword(s):

Phase Transition ◽

Honeycomb Lattice ◽

Critical Surface ◽

Similar Model ◽

Adsorption Phase ◽

International Audience ◽

Self Avoiding Walk ◽

Self Avoiding Walks

International audience In a recent paper with Bousquet-Mélou, de Gier, Duminil-Copin and Guttmann (2012), we proved that a model of self-avoiding walks on the honeycomb lattice, interacting with an impenetrable surface, undergoes an adsorption phase transition when the surface fugacity is 1+√2. Our proof used a generalisation of an identity obtained by Duminil-Copin and Smirnov (2012), and confirmed a conjecture of Batchelor and Yung (1995). Here we consider a similar model of self-avoiding walk adsorption on the honeycomb lattice, but with the impenetrable surface placed at a right angle to the previous orientation. For this model there also exists a conjecture for the critical surface fugacity, made by Batchelor, Bennett-Wood and Owczarek (1998). We adapt the methods of the earlier paper to this setting in order to prove the critical surface fugacity, but have to deal with several subtle complications which arise. This article is an abbreviated version of a paper of the same title, currently being prepared for submission.

Download Full-text

Alternation of up and down states at a dynamical phase-transition of a neural network with spatiotemporal attractors

Frontiers in Systems Neuroscience ◽

10.3389/fnsys.2014.00088 ◽

2014 ◽

Vol 8 ◽

Cited By ~ 11

Author(s):

Silvia Scarpetta ◽

Antonio de Candia

Keyword(s):

Neural Network ◽

Phase Transition ◽

Dynamical Phase Transition ◽

Dynamical Phase ◽

Up And Down States

Download Full-text

THE PHASE TRANSITION IN HUMAN COGNITION

New Mathematics and Natural Computation ◽

10.1142/s1793005709001234 ◽

2009 ◽

Vol 05 (01) ◽

pp. 197-220 ◽

Cited By ~ 10

Author(s):

MICHAEL J. SPIVEY ◽

SARAH E. ANDERSON ◽

RICK DALE

Keyword(s):

Neural Network ◽

Dynamical System ◽

Phase Transition ◽

Cognitive Psychology ◽

Computational Neuroscience ◽

Response Selection ◽

Human Cognition ◽

Natural Explanation ◽

Linear Filters ◽

Traditional Approaches

This article attempts to build a bridge between cognitive psychology and computational neuroscience, perhaps allowing each group to understand the other's theoretical insights and sympathize with the other's methodological challenges. In briefly discussing a collection of conceptual demonstrations, neural network and dynamical system simulations, and human experimental results, we highlight the importance of the concept of phase transition to understand cognitive function. Our goal is to show that viewing cognition as a self-organizing process (involving phase transitions, criticality, and autocatalysis) affords a more natural explanation of these data over traditional approaches inspired by a sequence of linear filters (involving detection, recognition, and then response selection).

Download Full-text

Deep Neural Network Detects Quantum Phase Transition

Journal of the Physical Society of Japan ◽

10.7566/jpsj.87.033001 ◽

2018 ◽

Vol 87 (3) ◽

pp. 033001 ◽

Cited By ~ 13

Author(s):

Shunta Arai ◽

Masayuki Ohzeki ◽

Kazuyuki Tanaka

Keyword(s):

Neural Network ◽

Phase Transition ◽

Quantum Phase Transition ◽

Deep Neural Network ◽

Quantum Phase

Download Full-text

A generalized adsorption-phase transition model to describe adsorption rates in flexible metal organic framework RPM3-Zn

Dalton Transactions ◽

10.1039/c5dt03432a ◽

2016 ◽

Vol 45 (10) ◽

pp. 4242-4257 ◽

Cited By ~ 4

Author(s):

Angela D. Lueking ◽

Cheng-Yu Wang ◽

Sarmishtha Sircar ◽

Christopher Malencia ◽

Hao Wang ◽

...

Keyword(s):

Phase Transition ◽

Metal Organic Framework ◽

Transition Model ◽

Adsorption Phase ◽

Metal Organic ◽

Phase Transition Kinetics ◽

Flexible Metal ◽

Adsorption Rates ◽

Organic Framework

The rate of adsorption to a flexible metal-organic framework is described via generalization of the Avrami theory of phase transition kinetics.

Download Full-text

Phase transition encoded in neural network

Progress of Theoretical and Experimental Physics ◽

10.1093/ptep/ptz082 ◽

2019 ◽

Vol 2019 (8) ◽

Cited By ~ 7

Author(s):

Kouji Kashiwa ◽

Yuta Kikuchi ◽

Akio Tomiya

Keyword(s):

Neural Network ◽

Phase Transition ◽

Neural Networks ◽

Critical Temperature ◽

Disordered Phase ◽

The Neural Network ◽

Main Observation ◽

Trained Neural Network ◽

Physical Quantities

Abstract We discuss an aspect of neural networks for the purpose of phase transition detection. To this end, we first train the neural network by feeding Ising/Potts configurations with labels of temperature so that it can predict the temperature of the input. We do not explicitly supervise whether the configurations are in the ordered/disordered phase. Nevertheless, we can identify the critical temperature from the parameters (weights and biases) of the trained neural network. We attempt to understand how temperature-supervised neural networks capture information on the phase transition by paying attention to what quantities they learn. Our detailed analyses reveal that they learn different physical quantities depending on how well they are trained. The main observation in this study is how the weights in the trained neural network can have information on the phase transition in addition to temperature.

Download Full-text