The role of experiment in Galileo's physics

Segre, Michael

doi:10.1007/bf00357045

Cited by 24 publications

(3 citation statements)

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“…When studying these and other works with Agassi's aid (Segre 1980), I noticed that these seemingly conflicting views do not actually contradict each other. Settle, Drake and others had made a claim about Galileo's actual work procedures (such as the methods he applied in his laboratory), whereas Koyré spoke on the methodological plane, pondering much more generally about the role of experiments in Galileo's work and intellectual achievements.…”

Section: Galileo's Methods and The Rationale Of His Workmentioning

confidence: 99%

Agassi’s Contribution to the History of Science

Segre

2022

Philosophy of the Social Sciences

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Agassi has undertaken the challenge of performing a microanalysis of the works of several scientists, pointing out areas of complexity, raising questions, and criticizing current histories of science. Among the topics he has tackled are Bacon’s philosophy of science, Boyle’s ideology, the rationale of Galileo’s work, Newton’s declared methodology—influential, but misleading—, Faraday’s emancipatory enterprise; and the roots of the quantum revolution. He attempts to reconstruct what scientists did in the immediate context, rather than what they said they did, and highlights difficulties and points of scepticism. Agassi considers many neglected factors that influenced science, taking in the metaphysical, social, anthropological, and even psychological spheres.

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Section: Galileo's Methods and The Rationale Of His Workmentioning

confidence: 99%

Agassi’s Contribution to the History of Science

Segre

2022

Philosophy of the Social Sciences

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“…However, it appears that Galileo did indeed perform some other experiments skillfully using inclined planes to dilute the gravitational acceleration, thereby reducing speeds, as well as the associated air resistance involved in the experiments. Although there are grave doubts among professional historians of science whether Galileo ever performed any significant experiments at all [49], in the case of experiments with an inclined plane, it was at least demonstrated that Galileo's alleged experimental results can be reproduced using devices that are very similar to those described by Galileo [50,51].…”

Section: Appendix a Free Fall: A Historical Perspectivementioning

confidence: 99%

Free fall in KvN mechanics and Einstein's principle of equivalence

Sen,

Dhasmana,

Silagadze

2020

Preprint

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The implementation of Einstein's principle of equivalence in Koopman-von Neumann (KvN) mechanics is discussed. The implementation is very similar to the implementation of this principle in quantum mechanics. This is not surprising, because KvN mechanics provides a Hilbert space formulation of classical mechanics that is very similar to the quantum mechanical formalism.Both in KvN mechanics and quantum mechanics, a propagator in a homogeneous gravitational field is simply related with a free propagator. As a result, the wave function in a homogeneous gravitational field in a freely falling reference frame differs from the free wave function only in phase.Fisher information, which quantifies our ability to estimate mass from coordinate measurements, does not depend on the magnitude of the homogeneous gravitational field, and this fact constitutes the formulation of Einstein's principle of equivalence, which is valid both in quantum mechanics and KvN mechanics.

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“…According to physics folklore, Galileo discovered, through experimentation, that objects fall with the same constant acceleration, thus disproving Aristotle's theory of gravity, which stated that objects fall at different speeds depending on their mass. The leaning tower of Pisa is often the setting for this famous stunt, although there is little evidence such an experiment actually took place (Cooper, 1936 ; Adler and Coulter, 1978 ; Segre, 1980 ). Indeed, many historians consider it to have been a thought experiment rather than an actual physical test.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Physics From Data: Universal Laws and Discrepancies

Silva

Higdon

Brunton

et al. 2020

Front. Artif. Intell.

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Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles and governing equations from measurement data alone. However, positing a universal physical law from data is challenging without simultaneously proposing an accompanying discrepancy model to account for the inevitable mismatch between theory and measurements. By revisiting the classic problem of modeling falling objects of different size and mass, we highlight a number of subtle and nuanced issues that must be addressed by modern datadriven methods for the automated discovery of physics. Specifically, we show that measurement noise and complex secondary physical mechanisms, such as unsteady fluid drag forces, can obscure the underlying law of gravitation, leading to an erroneous model. Without proposing an appropriate discrepancy model to handle these drag forces, the data supports an Aristotelian, versus a Galilean, theory of gravitation. Using the sparse identification of nonlinear dynamics (SINDy) algorithm, with the additional assumption that each separate falling object is governed by the same physical law, we are able to identify a viable discrepancy model to account for the fluid dynamic forces that explain the mismatch between a posited universal law of gravity and the measurement data. This work highlights the fact that the simple application of ML/AI will generally be insufficient to extract universal physical laws without further modification.1. Introduction. The ability to derive governing equations and physical principles has been a hallmark feature of scientific discovery and technological progress throughout human history. Even before the scientific revolution, the Ptolemaic doctrine of the perfect circle [1, 2] provided a principled decomposition of planetary motion into a hierarchy of circles, i.e. a bona fide theory for planetary motion. The scientific revolution and the resulting development of calculus provided the mathematical framework and language to precisely describe scientific principles, including gravitation, fluid dynamics, electromagnetism, quantum mechanics, etc. With advances in data science over the past few decades, principled methods are emerging for such scientific discovery from time-series measurements alone. Indeed, across the engineering, physical and biological sciences, significant advances in sensor and measurement technologies have afforded unprecedented new opportunities for scientific exploration. Despite its rapid advancements and wide-spread deployment, machine learning (ML) and artificial intelligence (AI) algorithms for scientific discovery face significant challenges and limitations, including noisy and corrupt data, latent variables, multiscale physics, and the tendency for overfitting. In this manuscript, we revisit one of the classic problems of physics considered by Galileo and Newton, that of falling objects and gravitation. We demonstrate that a sparse regression framework is ideally suited for physics discovery, highlighting ...

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The role of experiment in Galileo's physics

Cited by 24 publications

References 0 publications

Agassi’s Contribution to the History of Science

Agassi’s Contribution to the History of Science

Free fall in KvN mechanics and Einstein's principle of equivalence

Discovery of Physics From Data: Universal Laws and Discrepancies

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