Tensor networks enable researchers to tackle quantum physics problems previously thought to be solvable only by quantum computers. Credit: Lucy Reading-Ikkanda/Simons Foundation By applying a 1980s ...
Learn machine learning from the ground up - using Python and a handful of fundamental tools. This repository contains a range of resources associated with the 2nd edition of the university textbook ...
Demonstrating real advantage of machine learning–enhanced Monte Carlo for combinatorial optimization
In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...
Abstract: Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such ...
Purdue University’s Artificial Intelligence Microcredentials offer quick and convenient online courses that cover the fundamentals of artificial intelligence and its applications. Every course ...
Abstract: We consider a class of stochastic optimal control problems for discrete-time linear systems whose objective is the characterization of control policies that will steer the probability ...
Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The ...
We present two frameworks for design optimization of a multi-chamber pneumatic-driven soft actuator to optimize its mechanical performance. The design goal is to achieve maximal horizontal motion of ...
Uncertainties are widespread in the optimization of process systems, such as uncertainties in process technologies, prices, and customer demands. In this paper, we review the basic concepts and recent ...
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