Israel’s breakthrough could speed up search, simulation and AI learning

The researchers gave an example from nature. Imagine a bee searching for nectar. Bees leave their starting point, the hive, and move on in search of their goal, a flower. Because the direction and location of the flower are not known in advance, bees choose a different path each time they leave the hive, and the time it takes to reach the flower is random. Now imagine that the bees return home from time to time to “reset” their movements. This kind of mechanism is called random reset.

For about a decade, scientists have known that many search processes can be sped up by a random reset, or “return to home.” However, until now, the reset and search processes were assumed to be independent of each other. This assumption made the mathematical analysis of the problem much easier, as it would have been too difficult to solve equations that accounted for the possibility that bees could adapt their reset strategies to environmental conditions. On the other hand, if bees are close enough to a flower that they can smell or see it, it’s clear that they don’t want to go home before visiting the flower.

In a new study, researchers from Tel Aviv University’s Department of Chemistry have succeeded in overcoming just this difficulty. They developed a method to predict how the adaptive resetting process affects bee movement. The method is very general and can be applied to any random process, including chemical reactions, changes in protein structure, algorithms governing queuing systems, and even the learning process of neural networks.

According to the researchers, this breakthrough has far-reaching implications for statistical physics and computational chemistry by allowing searches, calculations, and simulation processes to be optimized more accurately and efficiently. The central importance of this work is that it proposes a general method for predicting and controlling the behavior of complex out-of-equilibrium systems, changing dynamic systems that are a fundamental problem of modern science, while significantly reducing the need to perform extremely costly calculations.

The research team explains that “random reset” is a mechanism that stops certain processes and restarts them from a defined point. In recent years, this method has been found to significantly improve the exploration and target-reaching process, but until now it has been limited to cases where resets occur independently of the state of the system. New research breaks that limitation and presents a general framework in which the reset rate is adapted to the state of the process and its history in real time.

According to the researchers, the main innovation of the study is the ability to predict the behavior of a system under random resets by using a “reweighting” technique to analyze only the dynamics without resetting. This approach makes it possible to calculate important measures such as the average time required to reach a goal, its distribution, and steady state without having to run many complex simulations for each scenario separately.

The researchers demonstrated how the method can be applied to improve “smart” search strategies, for example when search agents such as animals or algorithms change their behavior depending on their proximity to a target. Using this method, we can now easily calculate how adapting the reset rate to environmental information affects the search time. This is a necessary step in designing better exploration strategies. Additionally, this work demonstrated the possibility of designing adaptive steady states that are not in perfect equilibrium for physical systems, which was not possible with previous reset techniques.

Additionally, the researchers integrated machine learning into the model and showed that a neural network can be trained to automatically learn the best reset strategy for a specific task. This application has significantly accelerated molecular dynamics simulations involving complex processes such as protein folding, an area of ​​great importance in medicine and biotechnology.

According to the researchers, this new approach opens up a wide range of research and application possibilities in statistical physics and computational chemistry, and is expected to provide an efficient tool for designing complex processes in non-equilibrium systems.