On the Practical Applications of Information Field Dynamics
Martin Dupont

TL;DR
This paper introduces and analyzes Information Field Dynamics (IFD), a Bayesian-based simulation scheme for PDEs, demonstrating its convergence, error scaling, and relation to traditional numerical methods through theoretical and prototype developments.
Contribution
It provides the first proof of convergence for IFD schemes, analyzes their error scaling, and develops prototype translation-invariant and SPH-like IFD schemes.
Findings
Translation-invariant schemes converge to true field behavior at high resolution.
Error scaling of IFD schemes is analogous to high-order finite differences.
Prototypes demonstrate practical implementation and stability of IFD methods.
Abstract
In this study we explore a new simulation scheme for partial differential equations known as Information Field Dynamics (IFD). Information field dynamics attempts to improve on existing simulation schemes by incorporating Bayesian field inference into the simulation scheme. The field inference is truly Bayesian and thus depends on a notion of prior belief. A number of results are presented, both theoretical and practical. Many small fixes and results on the general theory are presented, before exploring two general classes of simulation schemes that are possible in the IFD framework. For both, we present a set of theoretical results alongside the development of a prototype scheme. The first class of models corresponds roughly to traditional fixed-grid numerical PDE solvers. The prior Bayesian assumption in these models is that the fields are smooth, and their correlation structure does…
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Taxonomy
TopicsNeural Networks and Applications
