8,182 research outputs found
Exactly Conservative Integrators
Traditional numerical discretizations of conservative systems generically
yield an artificial secular drift of any nonlinear invariants. In this work we
present an explicit nontraditional algorithm that exactly conserves these
invariants. We illustrate the general method by applying it to the three-wave
truncation of the Euler equations, the Lotka--Volterra predator--prey model,
and the Kepler problem. This method is discussed in the context of symplectic
(phase space conserving) integration methods as well as nonsymplectic
conservative methods. We comment on the application of our method to general
conservative systems.Comment: 30 pages, postscript (1.3MB). Submitted to SIAM J. Sci. Comput
Hamiltonian closures for fluid models with four moments by dimensional analysis
Fluid reductions of the Vlasov-Amp{\`e}re equations that preserve the
Hamiltonian structure of the parent kinetic model are investigated. Hamiltonian
closures using the first four moments of the Vlasov distribution are obtained,
and all closures provided by a dimensional analysis procedure for satisfying
the Jacobi identity are identified. Two Hamiltonian models emerge, for which
the explicit closures are given, along with their Poisson brackets and Casimir
invariants
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
The GIST of Concepts
A unified general theory of human concept learning based on the idea that humans detect invariance patterns in categorical stimuli as a necessary precursor to concept formation is proposed and tested. In GIST (generalized invariance structure theory) invariants are detected via a perturbation mechanism of dimension suppression referred to as dimensional binding. Structural information acquired by this process is stored as a compound memory trace termed an ideotype. Ideotypes inform the subsystems that are responsible for learnability judgments, rule formation, and other types of concept representations. We show that GIST is more general (e.g., it works on continuous, semi-continuous, and binary stimuli) and makes much more accurate predictions than the leading models of concept learning difficulty,such as those based on a complexity reduction principle (e.g., number of mental models,structural invariance, algebraic complexity, and minimal description length) and those based on selective attention and similarity (GCM, ALCOVE, and SUSTAIN). GIST unifies these two key aspects of concept learning and categorization. Empirical evidence from three\ud
experiments corroborates the predictions made by the theory and its core model which we propose as a candidate law of human conceptual behavior
Energy preserving model order reduction of the nonlinear Schr\"odinger equation
An energy preserving reduced order model is developed for two dimensional
nonlinear Schr\"odinger equation (NLSE) with plane wave solutions and with an
external potential. The NLSE is discretized in space by the symmetric interior
penalty discontinuous Galerkin (SIPG) method. The resulting system of
Hamiltonian ordinary differential equations are integrated in time by the
energy preserving average vector field (AVF) method. The mass and energy
preserving reduced order model (ROM) is constructed by proper orthogonal
decomposition (POD) Galerkin projection. The nonlinearities are computed for
the ROM efficiently by discrete empirical interpolation method (DEIM) and
dynamic mode decomposition (DMD). Preservation of the semi-discrete energy and
mass are shown for the full order model (FOM) and for the ROM which ensures the
long term stability of the solutions. Numerical simulations illustrate the
preservation of the energy and mass in the reduced order model for the two
dimensional NLSE with and without the external potential. The POD-DMD makes a
remarkable improvement in computational speed-up over the POD-DEIM. Both
methods approximate accurately the FOM, whereas POD-DEIM is more accurate than
the POD-DMD
Tools in the orbit space approach to the study of invariant functions: rational parametrization of strata
Functions which are equivariant or invariant under the transformations of a
compact linear group acting in an euclidean space , can profitably
be studied as functions defined in the orbit space of the group. The orbit
space is the union of a finite set of strata, which are semialgebraic manifolds
formed by the -orbits with the same orbit-type. In this paper we provide a
simple recipe to obtain rational parametrizations of the strata. Our results
can be easily exploited, in many physical contexts where the study of
equivariant or invariant functions is important, for instance in the
determination of patterns of spontaneous symmetry breaking, in the analysis of
phase spaces and structural phase transitions (Landau theory), in equivariant
bifurcation theory, in crystal field theory and in most areas where use is made
of symmetry adapted functions.
A physically significant example of utilization of the recipe is given,
related to spontaneous polarization in chiral biaxial liquid crystals, where
the advantages with respect to previous heuristic approaches are shown.Comment: Figures generated through texdraw package; revised version appearing
in J. Phys. A: Math. Ge
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