The Theory of Connections. Connecting Points
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Abstract (arXiv)
This study introduces a procedure to obtain general expressions, y = f(x), subject to linear constraints on the function and its derivatives defined at specified values. These constrained expressions can be used describe functions with embedded specific constraints. The paper first shows how to express the most general explicit function passing through a single point in three distinct ways: linear, additive, and rational. Then, functions with constraints on single, two, or multiple points are introduced as well as those satisfying relative constraints. This capability allows to obtain general expressions to solve linear differential equations with no need to satisfy constraints (the "subject to:" conditions) as the constraints are already embedded in the constrained expression. In particular, for expressions passing through a set of points, a generalization of the Waring's interpolation form, is introduced. The general form of additive constrained expressions is introduced as well as a procedure to derive its coefficient functions, requiring the inversion of a matrix with dimensions as the number of constraints.
Key facts. Cited by 10 papers in the map (data through 13 Oct 2026). First posted on arXiv 21 Feb 2017.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 1; 2026: 3.
10 papers.
- 2023AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identificationcited by 29
- 2023An Extreme Learning Machine-Based Method for Computational PDEs in Higher Dimensionscited by 21
- 2022A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEscited by 11
- 2023AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regressioncited by 9
- 2024Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiologycited by 3
- 2025Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEscited by 1
- 2026Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beamcited by 1
- 2024Solving Functional Optimization with Deep Networks and Variational Principlescited by 0
- 2026Exact Constraint Enforcement in Physics-Informed Extreme Learning Machines using Null-Space Projection Frameworkcited by 0
- 2026Comparative Study of Bending Analysis using Physics-Informed Neural Networks and Numerical Dynamic Deflection in Perforated nanobeamcited by 0
- 2026Comparative Study of Bending Analysis using Physics-Informed Neural Networks and Numerical Dynamic Deflection in Perforated nanobeam15 Apr 2026
- 2026Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam8 Apr 2026
- 2026Exact Constraint Enforcement in Physics-Informed Extreme Learning Machines using Null-Space Projection Framework16 Jan 2026
- 2025Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs1 Oct 2025
- 2024Solving Functional Optimization with Deep Networks and Variational Principles8 Oct 2024
- 2024Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology13 Aug 2024
- 2023AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression21 Dec 2023
- 2023AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification29 Sep 2023
- 2023An Extreme Learning Machine-Based Method for Computational PDEs in Higher Dimensions13 Sep 2023
- 2022A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs6 Oct 2022