paper-with-me

홈 › Papers

The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications

2017-10-18 · James P. Crutchfield

The principle goal of computational mechanics is to define pattern and structure so that the organization of complex systems can be detected and quantified. Computational mechanics developed from efforts in the 1970s and early 1980s to identify strange attractors as the mechanism driving weak fluid turbulence via the method of reconstructing attractor geometry from measurement time series and in the mid-1980s to estimate equations of motion directly from complex time series. In providing a mathematical and operational definition of structure it addressed weaknesses of these early approaches to discovering patterns in natural systems. Since then, computational mechanics has led to a range of results from theoretical physics and nonlinear mathematics to diverse applications---from closed-form analysis of Markov and non-Markov stochastic processes that are ergodic or nonergodic and their measures of information and intrinsic computation to complex materials and deterministic chaos and intelligence in Maxwellian demons to quantum compression of classical processes and the evolution of computation and language. This brief review clarifies several misunderstandings and addresses concerns recently raised regarding early works in the field (1980s). We show that misguided evaluations of the contributions of computational mechanics are groundless and stem from a lack of familiarity with its basic goals and from a failure to consider its historical context. For all practical purposes, its modern methods and results largely supersede the early works. This not only renders recent criticism moot and shows the solid ground on which computational mechanics stands but, most importantly, shows the significant progress achieved over three decades and points to the many intriguing and outstanding challenges in understanding the computational nature of complex dynamic systems.

📄 PDF Abstract BibTeX arXiv:1710.06832

Code (0)

등록된 구현이 없습니다.

Tasks

Time SeriesTime Series Analysis

Similar Papers 제목 키워드 기반

Understanding and Designing Complex Systems: Response to "A framework for optimal high-level descriptions in science and engineering---preliminary report"

2014-12-30 · James P. Crutchfield, Ryan G. James, Sarah Marzen, Dowman P. Varn

We recount recent history behind building compact models of nonlinear, complex processes and identifying their relevant macroscopic patterns or "macrostates". We give a synopsis of computational mechanics, predictive rat…

Graph Signal Processing: History, Development, Impact, and Outlook

2023-03-21 · Geert Leus, Antonio G. Marques, José M. F. Moura, Antonio Ortega 외

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interac…

Graph Learning

International Trade and Intellectual Property

2025-06-21 · Gaetan de Rassenfosse

Intellectual property (IP) rules have the potential to shape cross-border trade far more than their legalistic origins might suggest. Drawing on three decades of evidence, this review shows that stronger IP rights simult…

Causal Inference

Exbodiment: The Mind Made Matter

2024-12-14 · David C. Krakauer

Exbodiment describes mind outsourced to engineered matter and how matter reeducates mind. The constraints of exbodied matter encode elements of thought, channel decision-making, and constitute an important part of an ext…

Decision Making

A Tribute to Phil Bourne -- Scientist and Human

2022-12-08 · Cameron Mura, Emma Candelier, Lei Xie

This Special Issue of Biomolecules, commissioned in honor of Dr. Philip E. Bourne, focuses on a new field of biomolecular data science. In this brief retrospective, we consider the arc of Bourne's 40-year scientific and …

ARC