1,134 research outputs found
Gauge covariant neural network for 4 dimensional non-abelian gauge theory
We develop a gauge covariant neural network for four dimensional non-abelian
gauge theory, which realizes a map between rank-2 tensor valued vector fields.
We find that the conventional smearing procedure and gradient flow for gauge
fields can be regarded as known neural networks, residual networks and neural
ordinal differential equations for rank-2 tensors with fixed parameters. In
terms of machine learning context, projection or normalization functions in the
smearing schemes correspond to an activation function in neural networks. Using
the locality of the activation function, we derive the backpropagation for the
gauge covariant neural network. Consequently, the smeared force in hybrid Monte
Carlo (HMC) is naturally derived with the backpropagation. As a demonstration,
we develop the self-learning HMC (SLHMC) with covariant neural network
approximated action for non-abelian gauge theory with dynamical fermions, and
we observe SLHMC reproduces results from HMC.Comment: 50 pages, 8 figure
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad
applicability in scientific research. These techniques are being applied across
the diversity of nuclear physics research topics, leading to advances that will
facilitate scientific discoveries and societal applications.
This Review gives a snapshot of nuclear physics research which has been
transformed by machine learning techniques.Comment: Comments are welcom
Recommended from our members
Loss surface of XOR artificial neural networks.
Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these landscapes using optimization tools developed for potential energy landscapes in molecular science. The number of local minima and transition states (saddle points of index one), as well as the ratio of transition states to minima, grow rapidly with the number of nodes in the network. There is also a strong dependence on the regularization parameter, with the landscape becoming more convex (fewer minima) as the regularization term increases. We demonstrate that in our formulation, stationary points for networks with N_{h} hidden nodes, including the minimal network required to fit the XOR data, are also stationary points for networks with N_{h}+1 hidden nodes when all the weights involving the additional node are zero. Hence, smaller networks trained on XOR data are embedded in the landscapes of larger networks. Our results clarify certain aspects of the classification and sensitivity (to perturbations in the input data) of minima and saddle points for this system, and may provide insight into dropout and network compression
Public Ethics as a Canadiana “Theologica Publica”
This paper explores how a paradigm of the “public commons” can describe the relationship and contribution of various ‘public’ actors. The paper will explore how we might think differently about ‘public(s)’ and why “public ethics” could serve as a distinctively Canadian ‘public theology’ of ‘belonging.’ Paper presented as “Capital Theology: Religion and Politics in the 21st Century” at the Canadian Theological Society. Ottawa, June 2015
A nice surprise? Predictive processing and the active pursuit of novelty
Recent work in cognitive and computational neuroscience depicts human brains as devices that minimize prediction error signals: signals that encode the difference between actual and expected sensory stimulations. This raises a series of puzzles whose common theme concerns a potential misfit between this bedrock informationtheoretic vision and familiar facts about the attractions of the unexpected. We humans often seem to actively seek out surprising events, deliberately harvesting novel and exciting streams of sensory stimulation. Conversely, we often experience some wellexpected sensations as unpleasant and to-be-avoided. In this paper, I explore several core and variant forms of this puzzle, using them to display multiple interacting elements that together deliver a satisfying solution. That solution requires us to go beyond the discussion of simple information-theoretic imperatives (such as 'minimize long-term prediction error') and to recognize the essential role of species-specific prestructuring, epistemic foraging, and cultural practices in shaping the restless, curious, novelty-seeking human mind
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Sampling from known probability distributions is a ubiquitous task in
computational science, underlying calculations in domains from linguistics to
biology and physics. Generative machine-learning (ML) models have emerged as a
promising tool in this space, building on the success of this approach in
applications such as image, text, and audio generation. Often, however,
generative tasks in scientific domains have unique structures and features --
such as complex symmetries and the requirement of exactness guarantees -- that
present both challenges and opportunities for ML. This Perspective outlines the
advances in ML-based sampling motivated by lattice quantum field theory, in
particular for the theory of quantum chromodynamics. Enabling calculations of
the structure and interactions of matter from our most fundamental
understanding of particle physics, lattice quantum chromodynamics is one of the
main consumers of open-science supercomputing worldwide. The design of ML
algorithms for this application faces profound challenges, including the
necessity of scaling custom ML architectures to the largest supercomputers, but
also promises immense benefits, and is spurring a wave of development in
ML-based sampling more broadly. In lattice field theory, if this approach can
realize its early promise it will be a transformative step towards
first-principles physics calculations in particle, nuclear and condensed matter
physics that are intractable with traditional approaches.Comment: 11 pages, 5 figure
Turning intractable counting into sampling: Computing the configurational entropy of three-dimensional jammed packings.
We present a numerical calculation of the total number of disordered jammed configurations Ω of N repulsive, three-dimensional spheres in a fixed volume V. To make these calculations tractable, we increase the computational efficiency of the approach of Xu et al. [Phys. Rev. Lett. 106, 245502 (2011)10.1103/PhysRevLett.106.245502] and Asenjo et al. [Phys. Rev. Lett. 112, 098002 (2014)10.1103/PhysRevLett.112.098002] and we extend the method to allow computation of the configurational entropy as a function of pressure. The approach that we use computes the configurational entropy by sampling the absolute volume of basins of attraction of the stable packings in the potential energy landscape. We find a surprisingly strong correlation between the pressure of a configuration and the volume of its basin of attraction in the potential energy landscape. This relation is well described by a power law. Our methodology to compute the number of minima in the potential energy landscape should be applicable to a wide range of other enumeration problems in statistical physics, string theory, cosmology, and machine learning that aim to find the distribution of the extrema of a scalar cost function that depends on many degrees of freedom.We acknowledge useful discussions with Daniel Asenjo, Carl Goodrich, Silke Henkes, and Fabien Paillusson. S.M. acknowledges financial support by the Gates Cambridge Scholarship. K.J.S. acknowledges support by the Swiss National Science Foundation under Grant No. P2EZP2-152188 and No. P300P2-161078. J.D.S. acknowledges support by Marie Curie Grant 275544. D.F. and D.J.W. acknowledge support by EPSRC Programme Grant EP/I001352/1, by EPSRC grant EP/I000844/1 (D.F.) and ERC Advanced Grant RG59508 (D.J.W.)This is the author accepted manuscript. The final version is available from the American Physical Society via http://dx.doi.org/10.1103/PhysRevE.93.01290
Exploring QCD matter in extreme conditions with Machine Learning
In recent years, machine learning has emerged as a powerful computational
tool and novel problem-solving perspective for physics, offering new avenues
for studying strongly interacting QCD matter properties under extreme
conditions. This review article aims to provide an overview of the current
state of this intersection of fields, focusing on the application of machine
learning to theoretical studies in high energy nuclear physics. It covers
diverse aspects, including heavy ion collisions, lattice field theory, and
neutron stars, and discuss how machine learning can be used to explore and
facilitate the physics goals of understanding QCD matter. The review also
provides a commonality overview from a methodology perspective, from
data-driven perspective to physics-driven perspective. We conclude by
discussing the challenges and future prospects of machine learning applications
in high energy nuclear physics, also underscoring the importance of
incorporating physics priors into the purely data-driven learning toolbox. This
review highlights the critical role of machine learning as a valuable
computational paradigm for advancing physics exploration in high energy nuclear
physics.Comment: 146 pages,53 figure
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