Evidence map›Paper›PMID 38062136›Full record

ArticleScientific reports2023

A robophysical model of spacetime dynamics.

Shengkai Li, Hussain N Gynai, Steven W Tarr, Emily Alicea-Muñoz, Pablo Laguna, Gongjie Li, Daniel I Goldman

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shengkai LiDepartment of Physics, Princeton University, Princeton, NJ, 08544, USA.
Hussain N GynaiSchool of Physics, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Steven W TarrSchool of Physics, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Emily Alicea-MuñozSchool of Physics, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Pablo LagunaDepartment of Physics, Center for Gravitational Physics, University of Texas at Austin, Austin, TX, 78712, USA.
Gongjie LiSchool of Physics, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Daniel I GoldmanSchool of Physics, Georgia Institute of Technology, Atlanta, GA, 30332, USA. daniel.goldman@physics.gatech.edu.

Funding

NASA 80NSSC20K0641
6 · The paper itself

Abstract

Systems consisting of spheres rolling on elastic membranes have been used to introduce a core conceptual idea of General Relativity: how curvature guides the movement of matter. However, such schemes cannot accurately represent relativistic dynamics in the laboratory because of the dominance of dissipation and external gravitational fields. Here we demonstrate that an "active" object (a wheeled robot), which moves in a straight line on level ground and can alter its speed depending on the curvature of the deformable terrain it moves on, can exactly capture dynamics in curved relativistic spacetimes. Via the systematic study of the robot's dynamics in the radial and orbital directions, we develop a mapping of the emergent trajectories of a wheeled vehicle on a spandex membrane to the motion in a curved spacetime. Our mapping demonstrates how the driven robot's dynamics mix space and time in a metric, and shows how active particles do not necessarily follow geodesics in the real space but instead follow geodesics in a fiducial spacetime. The mapping further reveals how parameters such as the membrane elasticity and instantaneous speed allow the programming of a desired spacetime, such as the Schwarzschild metric near a non-rotating blackhole. Our mapping and framework facilitate creation of a robophysical analog to a general relativistic system in the laboratory at low cost that can provide insights into active matter in deformable environments and robot exploration in complex landscapes.

Identifiers

PMID38062136
PMCPMC10703937

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.