A new theorem in particle physics enabled by machine discovery

AbstractA widespread objection to research on scientific discovery is that there has been a noticeable dearth of significant novel findings in domain sciences contributed by machine discovery programs. The implication is that the essential parts of the discovery process are not captured by these programs. The aim of this note is to document for the AI audience a novel finding in particle physics that was enabled by the machine discovery program PAULI reported previously. This finding consists of a theorem that expresses the minimum number of conservation laws that are needed, mathematically speaking, to account for any consistent experimental data on particle reactions. This note also reports how a puzzle raised by the theorem—its conflict with physics practice—is resolved

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Information technologies for astrophysics circa 2001

It is easy to extrapolate current trends to see where technologies relating to information systems in astrophysics and other disciplines will be by the end of the decade. These technologies include mineaturization, multiprocessing, software technology, networking, databases, graphics, pattern computation, and interdisciplinary studies. It is easy to see what limits our current paradigms place on our thinking about technologies that will allow us to understand the laws governing very large systems about which we have large datasets. Three limiting paradigms are saving all the bits collected by instruments or generated by supercomputers; obtaining technology for information compression, storage and retrieval off the shelf; and the linear mode of innovation. We must extend these paradigms to meet our goals for information technology at the end of the decade

Fundamental Symmetries and Interactions

In Nuclear Physics numerous possibilities exist to investigate fundamental symmetries and interactions. In particular, the precise measurements of properties of fundamental fermions, searches for new interactions in $\beta$-decays, and violations of discrete symmeties offer possibilities to search for physics beyond Standard Theory. Precise measurements of fundamental constants can be carried out. Low energy experiments allow to probe New Physics at mass scales far beyond the reach of present accelerators or such planned for the future and at which predicted new particles could be produced directly.Comment: Review talk at the International Nuclear Physics Conference INPC04, G\"oteborg, Swede