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12 results for G-families

New polynomials defined for quandle structures, enhancing graph invariants.

problem Enhancing the counting invariant for spatial graphs and handlebody-links.
method Introducing quandle polynomials and G-family polynomials for quandles, defining enhancements for invariants.
result New enhancements of the G-family counting invariant for trivalent spatial graphs and handlebody-links.

We introduce the notion of a GG-family of quandles which is an algebraic system whose axioms are motivated by handlebody-knot theory, and use it to construct invariants for handlebody-knots. Our invariant can detect the chiralities of some handlebody-knots including unknown ones.

2012-05-09abs ↗pdf ↗

We introduce several algebraic structures related to handlebody-knots, including GG-families of biquandles, partially multiplicative biquandles and group decomposable biquandles. These structures can be used to color the semiarcs in YY-oriented spatial trivalent graph diagrams representing S1S^1-oriented handlebody-k…

2016-02-18abs ↗pdf ↗

We define a functor Q\mathcal{Q} from the category of multiple conjugation biquandles to that of multiple conjugation quandles. We show that for any multiple conjugation biquandle XX, there is a one-to-one correspondence between the set of XX-colorings and that of Q(X)\mathcal{Q}(X)-colorings diagrammatically for any …

2018-02-08abs ↗pdf ↗

Generalizes quandle constructions and defines a multiplication that results in an abelian group.

problem Tackles the construction and multiplication of quandle structures.
method Defines a composition of quandle structures and proves conditions for it to form a quandle, then shows the resulting group is abelian.
result Multiplication of quandle structures results in an abelian group.

Squared families are a new model class derived from linear transformations, offering convenient properties and universal approximation.

problem Developing a new class of probability models that are easier to handle and have useful properties.
method Introducing squared families as families of probability densities obtained by squaring a linear transformation of a statistic, and showing their properties and applications.
result Squared families have convenient properties and can approximate target densities well.

New algorithms improve vascular flow simulations in aortic aneurysms.

problem Limited accuracy of MRI in hemodynamics, patient-specific flow boundary conditions, and CFD's computational demands.
method Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets) integrated with 3D Navier-Stokes equations.
result Improved computational efficiency and good agreement with CFD simulations.