physics

Physicomimetics

Physicomimetics is a physics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Physicomimetics rather than just read about it. In short: Physicomimetics is physics-based swarm (computational) intelligence. The word is derived from physike (φυσική, Greek for "the science of physics") and mimesis (μίμησις, Greek for "imitation").

Key takeaways

Physicomimetics belongs to physics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Physicomimetics to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Physicomimetics from memory before moving on to harder problems.

Reference excerpt

Physicomimetics is physics-based swarm (computational) intelligence. The word is derived from physike (φυσική, Greek for "the science of physics") and mimesis (μίμησις, Greek for "imitation"). Physicomimetrics is related to Biosemiotics and Swarm Intelligence.

Overview In response to growing concerns that single monolithic robotic vehicles are expensive, brittle, and vulnerable, there has been a trend towards the development of distributed networks of small, inexpensive vehicles. The capability of these networks to dynamically monitor and sense environmental conditions while maintaining cost-effectiveness, robustness, and flexibility, is considered to be among their greatest assets. Dynamic sensor networks are critically needed for various tasks such as search and rescue, surveillance, perimeter defense, locating and mapping of chemical and biological hazards, virtual space telescopes, automated assembly of micro-electromechanical systems, and medical surgery (e.g., with nanobots). The core technology used to achieve these goals is a novel approach referred to as "artificial physics" or "physicomimetics". With physicomimetics, robotic agents perceive and react to artificial physics forces. By synthesizing the appropriate virtual forces, various important task-driven behaviors can be effectively achieved, such as lattice-shaped distributed antennas, perimeter defense, and dynamic surveillance. Furthermore, the systems self-organize, can self-repair, and are fault-tolerant. Recently the paradigm has been adapted to function optimization. The motivation for this approach is that any system designed using the laws of physics is amenable to the full gamut of empirical, analytical, and theoretical analysis tools used by physicists. This approach was first introduced by Professors William Spear and Diana Spears at the Naval Research Laboratory and the University of Wyoming. The first paper on this approach was published by them in 1999 at the IEEE International Conference on Information, Intelligence, and Systems. and the title was "Using Artificial Physics to Control Agents".

Worked examples

Example 1 — a first encounter with Physicomimetics

Start with the simplest possible case. Write down what Physicomimetics claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In physics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Physicomimetics before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Physicomimetics ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Physicomimetics

In research: Physicomimetics appears in physics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Physicomimetics in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Physicomimetics is common in secondary-school and first-year university syllabi. It links to neighbouring topics Multi-robot systems, so understanding it makes those chapters shorter.
In everyday life: Look for Physicomimetics outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Physicomimetics in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Physicomimetics means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Physicomimetics out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Physicomimetics in simple terms?

Why does Physicomimetics matter?

Because it connects several physics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Physicomimetics?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Physicomimetics.