This is the 2nd part of a two course graduate sequence in analytical methods to solve partial differential equations of mathematical physics. Review of Separation of variables. Laplace Equation: ...
In this work, we frame PDE solving as tool invocation via LLM-driven agents and introduce PDE-Agent, the first toolchain-augmented multiagent collaboration framework, inheriting the reasoning capacity ...
When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. In October 2024, news broke that Facebook parent company Meta had cracked an "impossible" problem ...
Abstract: Solving partial differential equations (PDEs) is omnipresent in scientific research and engineering and requires expensive numerical iteration for memory and computation. The primary ...
Abstract: Solving Maxwell's equations is crucial in various fields, like electromagnetic scattering and antenna design optimization. Physics-informed neural networks (PINNs) have shown powerful ...
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential ...
DeepFlow is a user-friendly framework for solving partial differential equations (PDEs), such as the Navier-Stokes equations, using Physics-Informed Neural Networks (PINNs). It provides a ...
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