AI Feynman: a Physics-Inspired Method for Symbolic Regression

A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult test set, we improve the state of the art success rate from 15% to 90%.Comment: 15 pages, 2 figs. Our code is available at https://github.com/SJ001/AI-Feynman and our Feynman Symbolic Regression Database for benchmarking can be downloaded at https://space.mit.edu/home/tegmark/aifeynman.htm

On Friction Reduction by Surface Modifications in the TEHL Cam/Tappet-Contact-Experimental and Numerical Studies

The overall energy efficiency of machine elements and engine components could be improved by using new technologies such as surface modifications. In the literature, surface engineering approaches like micro-texturing and the application of diamond-like carbon (DLC) coatings were frequently studied separately, with focus on a specific model contact and lubrication conditions. The contribution of the current study is to elucidate and compare the underlying friction reduction mechanisms of the aforementioned surface modifications in an application-orientated manner. The study applied the operating conditions of the thermo-elastohydrodynamically lubricated (TEHL) cam/tappet-contact of the valve train. Therefore, tribological cam/bucket tappet component Stribeck tests were used to determine the friction behavior of ultrashort pulse laser fabricated microtextures and PVD/PECVD deposited silicon-doped amorphous carbon coatings. Moreover, advanced surface characterization methods, as well as numerical TEHL tribo-simulations, were utilized to explore the mechanisms responsible for the observed tribological effects. The results showed that the DLC-coating could reduce the solid and fluid friction force in a wide range of lubrication regimes. Conversely, micro-texturing may reduce solid friction while increasing the fraction of fluid friction

Preferred Leadership Styles Within Minor League Baseball Organizations’ Front Offices

Due to the importance of leadership within athletics, this study set out to measure self-reported preference of leadership styles within Minor League Baseball (MiLB) organizations’ front offices. The Revised Leadership Scale for Sport (RLSS) was administered to MiLB front office employees at the AAA and AA levels. This instrument previously had been used to measure current athletes’ preferences for their coaches’ leadership styles. Four hypotheses focused on respondents’ preferences for their supervisors’ uses of autocratic and democratic leadership styles based on respondents’ gender and history with team or individual sport competition. Hypothesis testing revealed only one significant finding, that male front office employees had a higher preference for autocratic leadership style than did female front office employees