activity
20182021
most citedThe Multivariate Theory of Functional Connections: Theory, Proofs, and Application in Partial Differential Equations

45 citations · 86 across the 5 of their papers we have counts for

collaborators

12 papers

math.AP2021

The Multivariate Theory of Functional Connections: An n-Dimensional Constraint Embedding Technique Applied to Partial Differential Equations

Carl Leake

The Theory of Functional Connections (TFC) is a functional interpolation framework founded upon the so-called constrained expression: a functional that expresses the family of all…

astro-ph.EP202120 cited

Fast 2-impulse non-Keplerian orbit-transfer using the Theory of Functional Connections

Allan K. de Almeida Junior, Hunter Johnston, Carl Leake +1

This study applies a new approach, the Theory of Functional Connections (TFC), to solve the two-point boundary-value problem (TPBVP) in non-Keplerian orbit transfer. The perturbati…

cs.RO2020

Motivations and Preliminary Design for Mid-Air Deployment of a Science Rotorcraft on Mars

Jeff Delaune, Jacob Izraelevitz, Larry A. Young +18

Mid-Air Deployment (MAD) of a rotorcraft during Entry, Descent and Landing (EDL) on Mars eliminates the need to carry a propulsion or airbag landing system. This reduces the total…

math.NA202045 cited

The Multivariate Theory of Functional Connections: Theory, Proofs, and Application in Partial Differential Equations

Carl Leake, Hunter Johnston, Daniele Mortari

This article presents a reformulation of the Theory of Functional Connections: a general methodology for functional interpolation that can embed a set of user-specified linear cons…

cs.LG202011 cited

Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations

Enrico Schiassi, Carl Leake, Mario De Florio +3

In this work we present a novel, accurate, and robust physics-informed method for solving problems involving parametric differential equations (DEs) called the Extreme Theory of Fu…

cs.DS20203 cited

Random Sampling using k-vector

David Arnas, Carl Leake, Daniele Mortari

This work introduces two new techniques for random number generation with any prescribed nonlinear distribution based on the k-vector methodology. The first approach is based on in…