Deep active learning for complex systems

Inferring optimal solutions from limited data is considered the ultimate goal in scientific discovery. Artificial intelligence offers a promising avenue to greatly accelerate this process. Existing methods often depend on large datasets, strong assumptions about objective functions, and classic machine learning techniques, restricting their effectiveness to low-dimensional or data-rich problems. We present a deep active learning pipeline that combines deep neural networks with a novel tree search to find superior solutions in high-dimensional complex problems with non-cumulative objectives and limited data availability. Our pipeline iteratively approaches the optimum using a neural surrogate and introduces new search mechanisms to bypass the local optimum and minimize the number of samples needed to achieve superior solutions. These contributions enable our pipeline to achieve superior solutions across diverse problems with up to 2,000 dimensions, whereas existing methods are limited to 100 dimensions and require up to 10 times more data points. Our pipeline demonstrates wide applicability, discovering superior solutions in various domain science problems. This advancement enables data-efficient knowledge discovery and opens the path towards scalable self-driving laboratories. Although we focus on problems within the realm of scientific domain, the advancements achieved herein are applicable to a broader spectrum of challenges across all quantitative disciplines.