GitHub

PDE-Agent: A toolchain-augmented multi-agent framework for PDE solving

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 ...
GitHub

README.md

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 ...
IEEE

Numerical Simulations of Solving Heat-Conduction PDE (Partial Differential Equation) Using ZT (Zhang-Taylor) Time-Discretization Formula Extended from Neurodynamics

Abstract: Partial differential equations (PDEs) provide an accurate representation of mathematical and physical relationships in many modern engineering applications. In this paper, we utilize the ...
UMass Lowell

Mathematics Colloquium: Solving Partial Differential Equations (PDEs) Using Neural Networks 10/8

Abstract: There has been significant recent work on solving PDEs using neural networks on infinite dimensional spaces. In this talk we consider two examples. First, we prove that transformers can ...
acm.org

Machine Learning Framework Integrates Geometry into Fast PDE Solving

Partial differential equations (PDEs) are workhorses of science and engineering. They describe a vast range of phenomena, from flow around a ship’s hull, to acoustics in a concert hall, to heat ...
marktechpost

Evaluating the Efficacy of Machine Learning in Solving Partial Differential Equations: Addressing Weak Baselines and Reporting Biases

Machine Learning ML offers significant potential for accelerating the solution of partial differential equations (PDEs), a critical area in computational physics. The aim is to generate accurate PDE ...