Neural-network agents trained in social environments learn hybrid navigation strategies that combine individual landmark use with social following, with strategy shifts driven by the ratio of skilled to unskilled social agents.
A New Treatment of Boundary Conditions in PDE Solution with Galerkin Methods via Partial Integral Equation Framework
2 Pith papers cite this work, alongside 330 external citations. Polarity classification is still indexing.
abstract
We present a new analytical and numerical framework for solution of Partial Differential Equations (PDEs) that is based on an exact transformation that moves the boundary constraints into the dynamics of the corresponding governing equation. The framework is based on a Partial Integral Equation (PIE) representation of PDEs, where a PDE equation is transformed into an equivalent PIE formulation that does not require boundary conditions on its solution state. The PDE-PIE framework allows for a development of a generalized PIE-Galerkin approximation methodology for a broad class of linear PDEs with non-constant coefficients governed by non-periodic boundary conditions, including, e.g., Dirichlet, Neumann and Robin boundaries. The significance of this result is that solution to almost any linear PDE can now be constructed in a form of an analytical approximation based on a series expansion using a suitable set of basis functions, such as, e.g., Chebyshev polynomials of the first kind, irrespective of the boundary conditions. In many cases involving homogeneous or simple time-dependent boundary inputs, an analytical integration in time is also possible. We present several PDE solution examples in one spatial variable implemented with the developed PIE-Galerkin methodology using both analytical and numerical integration in time. The developed framework can be naturally extended to multiple spatial dimensions and, potentially, to nonlinear problems.
years
2026 2representative citing papers
Long-lasting evolutions in Conway's Game of Life are framed as temporal retention of information, proposed as an informational biosignature by reversing the authors' prior logic on lifelike cellular automata.
citing papers explorer
-
Social-spatial dependencies for learning visual navigation
Neural-network agents trained in social environments learn hybrid navigation strategies that combine individual landmark use with social following, with strategy shifts driven by the ratio of skilled to unskilled social agents.
-
Temporal Retention of Information as a Biosignature
Long-lasting evolutions in Conway's Game of Life are framed as temporal retention of information, proposed as an informational biosignature by reversing the authors' prior logic on lifelike cellular automata.