---
title: "Physics-informed traditional and generative Mori-Zwanzig-reduced models for multiscale systems"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05211v1"
permalink: "https://feed.astralor.com/items/itm_b24b24950583979f5cb60864"
published: "2026-11-03T16:57:56.000Z"
score: 55
channel: "ai"
tags: ["Physics-informed","Mori-Zwanzig-reduced","Model","Mori-Zwanzig","Fourier"]
---

# Physics-informed traditional and generative Mori-Zwanzig-reduced models for multiscale systems

## At a glance

Model reduction via the Mori-Zwanzig (MZ) formalism is well understood for coarse-grained representations constructed from standard basis functions such as Fourier functions. We show that the same formalism can be used …

## Key points

- Model reduction via the Mori-Zwanzig (MZ) formalism is well understood for coarse-grained representations constructed from standard basis functions such as Fourier functions.
- We show that the same formalism can be used to build reduced-order models in bases discovered by physics-informed machine learning.
- The fluctuation-dissipation theorem (FDT) is used to close the resulting non-Markovian evolution equations for the expansion coefficients of the learned basis.

## Full text

Model reduction via the Mori-Zwanzig (MZ) formalism is well understood for coarse-grained representations constructed from standard basis functions such as Fourier functions. We show that the same formalism can be used to build reduced-order models in bases discovered by physics-informed machine learning. The fluctuation-dissipation theorem (FDT) is used to close the resulting non-Markovian evolution equations for the expansion coefficients of the learned basis. We further introduce a complementary, generative closure model. Relative to the Markovian dynamics alone, both the MZ and generative closures substantially improve predictions of the filtered states.

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