Over the last fifty years, there have been numerous efforts to develop comprehensive discrete formulations of geometry and physics from first principles: from Whitney's geometric integration theory [33] to Harrison's theory of chainlets [16], including Regge calculus in general relativity [26, 34], Tonti's work on the mathematical structure of physical theories [30] and their discrete formulation [31], plus multifarious researches into so-called mimetic discretization methods [28], discrete exterior calculus [11, 12], and discrete differential geometry [2, 10]. All these approaches strive to tell apart the different mathematical structures---topological, differentiable, metrical---underpinning a physical theory, in order to make the relationships between them more transparent. While each component is reasonably well understood, computationally effective connections between them are not yet well established, leading to difficulties in combining and progressively refining geometric models and physics-based simulations. This paper proposes such a connection by introducing the concept of metrized chains, meant to establish a discrete metric structure on top of a discrete measure-theoretic structure embodied in the underlying notion of measured (real-valued) chains. These, in turn, are defined on a cell complex, a finite approximation to a manifold which abstracts only its topological properties.

In this paper, we show that the (co)chain complex associated with a decomposition of the computational domain, commonly called a mesh in computational science and engineering, can be represented by a block-bidiagonal matrix that we call the Hasse matrix. Moreover, we show that topology-preserving mesh refinements, produced by the action of (the simplest) Euler operators, can be reduced to multilinear transformations of the Hasse matrix representing the complex. Our main result is a new representation of the (co)chain complex underlying field computations, a representation that provides new insights into the transformations induced by local mesh refinements. Our approach is based on first principles and is general in that it applies to most representational domains that can be characterized as cell complexes, without any restrictions on their type, dimension, codimension, orientability, manifoldness, and connectedness.

In this paper we show that the (co)chain complex associated with a decomposition of the computational domain, commonly called a mesh in computational science and engineering, can be represented by a block-bidiagonal matrix that we call the Hasse matrix. Moreover, we show that topology-preserving mesh refinements, produced by the action of (the simplest) Euler operators, can be reduced to multi-linear transformations of the Hasse matrix representing the complex.

This article presents a functional programming approach to geometric design with embedded polyhedral complexes. Its main goals are to show the expressive power of the language as well as its usefulness for geometric design. The language, named PLASM (the Programming LAnguage for Solid Modeling), introduces a very high level approach to “constructive” or “generative” modeling. Geometrical objects are generated by evaluating some suitable language expressions. Because generating expressions can be easily combined, the language also extends the standard variational geometry approach by supporting classes of geometric objects with varying topology and shape. The design language PLASM can be roughly considered as a geometry-oriented extension of a subset of the functional language FL. The language takes a dimension-independent approach to geometry representation and algorithms. In particular it implements an algebraic calculus over embedded polyhedra of any dimension. The generated objects are always geometrically consistent because the validity of geometry is guaranteed at a syntactical level. Such an approach allows one to use a representation scheme which is weaker than those usually adopted in solid modelers, thus encompassing a broader geometric domain, which contains solids, surfaces, and wire-frames, as well as higher-dimensional objects.

Dealing with simplicial decompositions which are dimension independent allows for the convergence of disparate viewpoints from computer graphics, solid and geometric modeling.In this framework it is possible to treat in a unified manner several geometric problems, such as solid modeling of articulated objects, simplicial approximation of curved manifolds, motion encoding and interference detection, free configuration space computation, and graphical representation of multidimensional data.In the paper the authora describe the winged scheme, a simple representation based on simplicial decompositions, which can be used for linear polyhedra of any dimension and which allows for "solid" approximation of curved manifolds when combined with curved maps (e.g., NURBS).Various operators and algorithms are discussed, including boundary evaluation, linear and screw extrusion, grid generation, simplicial maps, and set operations.A simple manipulation language is also introduced, and some nontrivial examples are discussed.

Abstract In designing graphical workstations for high‐level performance presentations of graphical models, the main goal nowadays is to perform some effective real‐time traversal and rendering of data. In this paper we present a reference model of parallel processing for graphics, taking into account the main aspects of colour rendering of solid hierarchical shapes; i.e., traversal of structures, hidden surfaces removal, shadow identification and light models. In particular, a parallel design of the main graphical algorithms is presented. Three abstract machines are proposed, designed respectively for data structure traversing, for depth ordering and shadows computation and finally for shape merging and screen processing. Such abstract machines, internally parallel, may be combined in various ways to define different physical architectures. CR Categories and Subject Descriptors : C.1.2 [Multiple Data Stream Architectures] MIMD Processors, Parallel Processors; 1.3.2 [Computer Graphics] Graphics Systems; 1.3.3 [Computer Graphics] Picture Generation ‐Display Algorithms, Viewing Algorithms; 1.3.7 [Computer Graphics] Three Dimensional Graphics and Realism ‐ Colour, Shading, Shadowing, Visible Line/Surface I Algorithms.