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I encountered 'coarse-grained' yesterday in Farrell and Shalizi's "AI as Social Technology":
...All such systems process information by reducing complex realities into more tractable 'coarse grainings' or abstractions..
The idea was clear and resonant, and 'granularity' has been a to-me-familiar descriptor/property for 50+ years, but I can't recall that I had encountered the bound phrase, and I surely hadn't understood its metaness: its centrality to statistics, modeling, AI, biology, physics, etc., and its connection to abstraction was novel. Perhaps my primary connection has been hands-on, via the many thousands of jpg images I have processed... [where might 'granularity' appear with MYKeywords?]{abstraction... Cubism... constructing the Surreal...} [rabbit holes everywhere]
The unpacking below is (as usual) provisional and in-process.
In no particular order to start with, but nudged into a trunk-to-tail line:
coarse-graining Wiktionary...The rescaling of a phenomenon into units or cells of size close to the uncertainty of measurement. (see also Granularity at Wikipedia)Q: "What kind of systems do we need to coarse-grain to observe interesting phenomena?"
A: Systems that would otherwise be too large and/or complicated to simulate in a reasonable amount of time and/or with a reasonable amount of computational resources...A2: As the systems get bigger and bigger and the computational resources, even with the GPU development, doesn't grow in the same speed, we need to use some approximations if we want to get some knowledge of the system behavior
What is the difference between fine-grained and coarse-grained entropy? stackexchange.com (2019)
So I am not a native English speaker, but I understand the words "coarse" and "fine" grained. But I really don't know what to make of them in the context of entropy. I have encountered this many times in the Preskill notes, but now these terms also appears in one paper I have to read and I really do not understand it. Can someone explain?'Coarse-graining' can help scientists understand complex microbial ecosystems, theory suggests Talia Ogliore at phys.org (2022)
..."Put simply, coarse-graining means omitting details, and we say that is fine if it doesn't change the outcome of a specified experiment," Tikhonov said."Note that this definition links coarse-grainability to a choice of an experiment, and that's actually important," he said. "We argue that coarse-grainability fundamentally depends on which aspect of the ecosystem you care about. We show that the exact same ecosystem can be readily coarse-grainable if you're interested in one property, whereas for another, you may need to know all the details."
Coarse-graining Jessica Flack at edge.org (2017) (see a whole catalog of contributions to WHAT SCIENTIFIC TERM OR CONCEPT OUGHT TO BE MORE WIDELY KNOWN?)
In physics a fine-grained description of a system is a detailed description of its microscopic behavior. A coarse-grained description is one in which some of this fine detail has been smoothed over.Coarse-graining is at the core of the second law of thermodynamics, which states that the entropy of the universe is increasing. As entropy, or randomness, increases there is a loss of structure. This simply means that some of the information we originally had about the system has become no longer useful for making predictions about the behavior of a system as a whole.
❧ ❧ ❧ ❧ ❧ Everything You Want to Know About Coarse-Graining and Never Dared to Ask: Macromolecules as a Key Example Marina G. Guenza at Wiley Interdisciplinary Reviews (2025)
Coarse-graining (CG) is transforming the study of molecular systems, allowing researchers to explore by computer simulations larger and more complex structures than ever before. Continued advancements in CG techniques are making simulations more efficient, establishing this approach as a cornerstone for designing innovative materials and eco-friendly alternatives to traditional plastics. Additionally, CG methods are becoming indispensable for unraveling the complexities and functional mechanisms of large-scale macromolecular machines within cells.Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models Timothy D Loose et al. Jour Phys Chem (2023)
Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits of which the most significant is accuracy. Neural networks can inherently incorporate multibody effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology.Coarse-graining methods for computational biology Marissa G Saunders and Gregory A Voth Ann Rev Biophysics (2013)
Connecting the molecular world to biology requires understanding how molecular-scale dynamics propagate upward in scale to define the function of biological structures. To address this challenge, multiscale approaches, including coarse-graining methods, become necessary. We discuss here the theoretical underpinnings and history of coarse-graining and summarize the state of the field, organizing key methodologies based on an emerging paradigm for multiscale theory and modeling of biomolecular systems.Theory of Coarse-Graining and Multiscale Phenomena The Voth Group at uchicago.edu
Coarse-graining of complex systems Daniel Little at Understanding Society (2016)
The question of the relationship between micro-level and macro-level is just as important in physics as it is in sociology. Is it possible to derive the macro-states of a system from information about the micro-states of the system? It turns out that there are some surprising aspects of the relationship between micro and macro that physical systems display. The mathematical technique of "coarse-graining" represents an interesting wrinkle on this question. So what is coarse-graining? Fundamentally it is the idea that we can replace micro-level specifics with local-level averages, without reducing our ability to calculate macro-level dynamics of behavior of a system.Generative Coarse-Graining Wang, Wujie at harvard.edu (2022)
In theoretical and computational science, order reduction or coarse-graining (CG) is a popular approach to describe complex systems with lower-dimensional representations that capture the key physics. In modeling the dynamics of large biological and materials systems at the molecular level, CG simulations unify selected groups of atoms into CG particles described by simpler equations of motions than the all-atom systemCoarse-graining is at the core of the second law of thermodynamics, which states that the entropy of the universe is increasing. It is important to recognize that a critical property of a coarse-grained description is that it is "true" to the system, meaning that it is a reduction or simplification of the actual microscopic details. It involves integrating over component behaviour. A wonderful intuitive description can be found in this answer.
Wikipedia says:
The exactly evolving entropy, which does not increase, is known as fine-grained entropy. The blurred entropy is known as coarse-grained entropy. Leonard Susskind in this lecture analogizes this distinction to the notion of the volume of a fibrous ball of cotton: On one hand the volume of the fibres themselves is constant, but in another sense, there is a larger coarse-grained volume, corresponding to the outline of the ball.Data-driven coarse graining of large biomolecular structures Yi-Ling Chen and Michael Habeck at plos.org (2017)
Advances in experimental and computational techniques allow us to study the structure and dynamics of large biomolecular assemblies at increasingly higher resolution. However, with increasing structural detail it can be challenging to unravel the mechanism underlying the function of molecular machines. One reason is that atomistic simulations become computationally prohibitive. Moreover it is difficult to rationalize the functional mechanism of systems composed of tens of thousands to millions of atoms by following each atom's movements. Coarse graining (CG) allows us to understand biological structures from a hierarchical perspective and to gradually zoom into the adequate level of structural detail. This article introduces a Bayesian approach for coarse graining biomolecular structures.