[{"data":1,"prerenderedAt":114},["ShallowReactive",2],{"docs-\u002Fdocs\u002Fcore-concepts":3,"docs-navigation":93,"docs-surround-\u002Fdocs\u002Fcore-concepts":109},{"id":4,"title":5,"body":6,"description":84,"extension":85,"meta":86,"navigation":87,"path":88,"seo":89,"stem":90,"updated":91,"__hash__":92},"docs\u002Fdocs\u002F2.core-concepts.md","Core concepts",{"type":7,"value":8,"toc":77},"minimark",[9,13,18,26,30,41,52,56,67,70,74],[10,11,12],"p",{},"A handful of terms carry most of the platform. They compose cleanly: sensors feed\nexperiments, experiments produce runs, and process-class templates make runs comparable\nacross the fleet.",[14,15,17],"h2",{"id":16},"sensor-arrays","Sensor arrays",[10,19,20,21,25],{},"A ",[22,23,24],"strong",{},"sensor array"," is a set of inputs to a system — built from physical hardware, replay\nof recorded data, or model-backed virtual sensors. The abstraction is the same across all\nthree, so an experiment does not care whether a channel is a real thermocouple or a\nsimulated one.",[14,27,29],{"id":28},"experiments-and-runs","Experiments and runs",[10,31,32,33,36,37,40],{},"An ",[22,34,35],{},"experiment"," is the definition of something to exercise — the sensors, the models in\nthe loop, and the conditions to apply. A ",[22,38,39],{},"run"," is one execution of an experiment on the\nedge, in real, simulated, or hybrid mode. Runs are deterministic and replayable: given a\nseed and a fixed timestep, a simulated run reproduces.",[42,43,49],"pre",{"className":44,"code":46,"language":47,"meta":48},[45],"language-text","experiment (definition)  ──run──▶  run #1  (edge A, real)\n                          ──run──▶  run #2  (edge A, simulated)\n                          ──run──▶  run #3  (edge B, hybrid)\n","text","",[50,51,46],"code",{"__ignoreMap":48},[14,53,55],{"id":54},"process-class-templates","Process-class templates",[10,57,20,58,61,62,66],{},[22,59,60],{},"process-class template"," describes a ",[63,64,65],"em",{},"kind"," of process, independent of any single\nedge. When runs across the fleet instantiate the same template, they become comparable —\nthat shared identity is the join key the cloud aggregates on.",[10,68,69],{},"This is what makes fleet-level learning possible: without a common template identity,\nruns on different edges are just unrelated time series. With it, they are a comparable\nset the cloud can aggregate and, in the model-loop phase, learn from.",[14,71,73],{"id":72},"how-they-fit-together","How they fit together",[10,75,76],{},"Sensors define what you measure; experiments define what you do with them; runs are the\nrecord of doing it; and templates are what let many runs — across many edges — line up\ninto something a fleet can improve against.",{"title":48,"searchDepth":78,"depth":78,"links":79},2,[80,81,82,83],{"id":16,"depth":78,"text":17},{"id":28,"depth":78,"text":29},{"id":54,"depth":78,"text":55},{"id":72,"depth":78,"text":73},"The vocabulary of TameChaos — sensor arrays, experiments, runs, and the process-class templates that make runs comparable across a fleet.","md",{},true,"\u002Fdocs\u002Fcore-concepts",{"title":5,"description":84},"docs\u002F2.core-concepts","2026-06-01","4Uu7Gb64QoDkEKUsLN5sLsREz_gZqovOBWaYRUqJJ8Q",[94],{"title":95,"path":96,"stem":97,"children":98,"page":108},"Docs","\u002Fdocs","docs",[99,103,104],{"title":100,"path":101,"stem":102},"Getting started","\u002Fdocs\u002Fgetting-started","docs\u002F1.getting-started",{"title":5,"path":88,"stem":90},{"title":105,"path":106,"stem":107},"Edge and cloud","\u002Fdocs\u002Fedge-and-cloud","docs\u002F3.edge-and-cloud",false,[110,112],{"title":100,"path":101,"stem":102,"description":111,"children":-1},"A short orientation to TameChaos — what the platform is, how the edge and cloud divide responsibility, and where to go next.",{"title":105,"path":106,"stem":107,"description":113,"children":-1},"Which responsibilities sit on the authoritative edge and which sit in the non-authoritative cloud — and why distribution is publish-and-pull, never command.",1780537673022]