Researchers at Forschungszentrum Jülich have developed an AI capable of formulating physical theories by recognizing patterns in complex data sets, a feat historically achieved by great physicists like Isaac Newton and Albert Einstein. This AI, part of the “Physics of AI” initiative, simplifies complex interactions in data to develop new theories, differing from conventional approaches by making the theories explainable and grounded in the language of physics. Credit: SciTechDaily.
In the following interview, Prof. Moritz Helias from Forschungszentrum Jülich’s Institute for Advanced Simulation explains what the “Physics of AI” is all about and to what extent it differs from conventional approaches.You usually start with observations of the system before attempting to propose how the different system components interact with each other in order to explain the observed behavior. New predictions are then derived from this and put to the test.
Reference: “Learning Interacting Theories from Data” by Claudia Merger, Alexandre René, Kirsten Fischer, Peter Bouss, Sandra Nestler, David Dahmen, Carsten Honerkamp and Moritz Helias, 20 November 2023,Millenials c2024: OH ZOWEE! AI is gonna replace physicists and probably humans!Whether it is humans or artificial intelligence, mathematics and mathematical models are the foundation for their understanding and interpretation of nature.
The branch of geometry known as topology has become a cornerstone of modern physics. Topological vortex and antivortex are two bidirectional coupled continuous chaotic systems. They exhibit parity conservation, charge conjugation, and time reversal symmetry. The synchronization effect is extremely important in their interactions. The synchronization effect of the superposition, deflection, and twisting of multiple or countless topological vortices will make spacetime motion more complex.