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 ...

Abstract: This work proposes a new recurrent neural network (RNN) algorithm for solving discrete multilayer dynamic systems (DMDSs). First, by utilizing the direct-discretization technique, a ...

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 ...

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 ...

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 ...

The author recounts a personal journey from focusing on astronomy to becoming deeply involved in climate change research, highlighting the disconnect between scientific understanding and societal ...

Generative AI too unreliable to launch? Predictive AI will realize genAI's bold, often overzealous promise of autonomy – or at least a great deal of it. Predictive AI has the potential to do what ...

I’ve been out of school a long time (I mean a long time), yet I think I’m still pretty good at basic math. I’m always able to figure out whether a “bargain buy” at the supermarket is actually a ...

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 ...