Statistical physics of design

Klishin, Andrei A.; Shields, Colin P.F.; Anders, Greg van

doi:10.1088/1367-2630/aae72a

Cited by 4 publications

(11 citation statements)

References 29 publications

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“…Large-scale effects on unit routing induced by changes in design pressure were studied in Ref. 11 . Here, we study this model at the mesoscale to understand the robustness of system design.…”

Section: Example System: Model Results and Discussionmentioning

confidence: 99%

“…Free energy ideas have been used to describe collective behavior in biological swarms 32 and and in brain activity 33 . Work has also shown 11 that statistical physics approaches can be applied to systems design problems, under the guise of Systems Physics. Here, we use Systems Physics to study the intermediate-scale structure of design spaces to study design robustness.…”

Section: Robust Design From Statistical Physicsmentioning

confidence: 99%

“…Our mathematical model expands upon Systems Physics, a statistical mechanics framework for design problems introduced in Ref. 11 . We consider a design problem with a combinatorially large ensemble of candidate detailed design solutions .…”

Section: Robust Design From Statistical Physicsmentioning

confidence: 99%

“…Selecting particular values of significantly shapes the probability distribution and can radically and abruptly change the types of the preferred designs . Predicting and quantifying the preferred designs within the ensemble is a goal of Systems Physics 11 .…”

Section: Robust Design From Statistical Physicsmentioning

confidence: 99%

“…Here, we operationalize questions about the robustness of subsystem design via the "Systems Physics" approach of Ref. 11 . Via Systems Physics we draw quantitative comparisons between design classes, or architectures, at intermediate, "mesoscale" levels of analysis.…”

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confidence: 99%

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Robust design from systems physics

Klishin

Kirkley

Anders

2020

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A crucial challenge in engineering modern, integrated systems is to produce robust designs. However, quantifying the robustness of a design is less straightforward than quantifying the robustness of products. For products, in particular engineering materials, intuitive, plain language terms of strong versus weak and brittle versus ductile take on precise, quantitative meaning in terms of stress–strain relationships. Here, we show that a “systems physics” framing of integrated system design produces stress–strain relationships in design space. From these stress–strain relationships, we find that both the mathematical and intuitive notions of strong versus weak and brittle versus directly characterize the robustness of designs. We use this to show that the relative robustness of designs against changes in problem objectives has a simple graphical representation. This graphical representation, and its underlying stress–strain foundation, provide new metrics that can be applied to classes of designs to assess robustness from feature- to system-level.

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Section: Example System: Model Results and Discussionmentioning

confidence: 99%

Section: Robust Design From Statistical Physicsmentioning

confidence: 99%

Section: Robust Design From Statistical Physicsmentioning

confidence: 99%

Section: Robust Design From Statistical Physicsmentioning

confidence: 99%

mentioning

confidence: 99%

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Robust design from systems physics

Klishin

Kirkley

Anders

2020

Sci Rep

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No free lunch for avoiding clustering vulnerabilities in distributed systems

Chitnelawong,

Klishin,

Mackay

et al. 2024

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Emergent design failures are ubiquitous in complex systems, and often arise when system elements cluster. Approaches to systematically reduce clustering could improve a design’s resilience, but reducing clustering is difficult if it is driven by collective interactions among design elements. Here, we use techniques from statistical physics to identify mechanisms by which spatial clusters of design elements emerge in complex systems modelled by heterogeneous networks. We find that, in addition to naive, attraction-driven clustering, heterogeneous networks can exhibit emergent, repulsion-driven clustering. We draw quantitative connections between our results on a model system in naval engineering to entropy-driven phenomena in nanoscale self-assembly, and give a general argument that the clustering phenomena we observe should arise in many distributed systems. We identify circumstances under which generic design problems will exhibit trade-offs between clustering and uncertainty in design objectives, and we present a framework to identify and quantify trade-offs to manage clustering vulnerabilities.

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